Covid19Japan.com

Covid19 Japanが独自に収集している陽性者単位のデータ（個票データ）。ソースとデータは全てGitHubにて公開されており、データはJSON形式。「レコード数 \(\neq\) 累計陽性者数」であることに注意。

　

Import

Covid19 JapanがGitHubで公開しているデータは前述のようにJSON形式であり、最新データはlatest.jsonファイルで示されている。このため、読み込む際はひと工夫必要。

個票データ（Patient Data）

陽性者単位の個票データ。

path <- "https://raw.githubusercontent.com/reustle/covid19japan-data/master/docs/patient_data/"

df <- path %>% 
  paste0("latest.json") %>% 
  readr::read_lines() %>% 
  paste0(path, .) %>% 
  jsonlite::fromJSON()

df

　

集計データ（Summary Data）

死亡者数や重症者数などの推移データはsummaryフォルダ内のJSON形式ファイルにまとめられている。読み込むと分かるがリスト型で、その中データフレームが含まれる形式である。
summaryフォルダの他にsummary_minフォルダというフォルダがあるが、summary_minフォルダ内のJSONファイルは単に改行を省略して小さくしたファイル。

path <- "https://raw.githubusercontent.com/reustle/covid19japan-data/master/docs/summary/"

df_s <- path %>% 
  paste0("latest.json") %>% 
  readr::read_lines() %>% 
  paste0(path, .) %>% 
  jsonlite::fromJSON()

df_s %>% summary()

##             Length Class      Mode     
## prefectures 27     data.frame list     
## regions     12     data.frame list     
## daily       37     data.frame list     
## updated      1     -none-     character

　
三つのデータフレームと一つのベクトル（更新日時）から構成されている。データフレームは上から順に都道府県別、地方別、日次となっているが、Lengthを見てわかるようにそれぞれに含まれる集計データが異なっている。

　

都道府県単位集計

更新日時（$updated）における都道府県単位での累積値。厚生労働省がオープンデータから除いている空港検疫・ダイヤモンドプリンセス・長崎クルーズ船・その他が含まれるので全51区分になっている。

df_s$prefectures

陽性者・死亡者などの時系列集計データがネストされて格納されている。日付はネストされていないので、各項目に対するstartDateの項を参照すること。

項目	内容	備考
dailyConfirmedCount	陽性者数	単日
dailyConfirmedStartDate	陽性者数のカウント開始日	区分により開始日が異なる
dailyDeceasedCount	死亡者数	単日
dailyDeceasedStartDate	死亡者数のカウント開始日	区分により開始日が異なる
dailyRecoveredCumulative	快復者数	累計
dailyRecoveredStartDate	快復者数のカウント開始日	区分により開始日が異なる
dailyActive	治療者数1	単日
dailyActiveStartDate	治療者数のカウント開始日	区分により開始日が異なる

¹ 陽性者数から死亡者数と快復者数を引いた数値を治療者数としている

　

地方単位集計

更新日次時点における地方区分単位での累積値。陽性者の時系列集計データが都道府県単位データと同様にネストで格納されているが、死亡者・快復者・治療者のデータは含まれていない。
なお、時系列データの合計値と累積項の値が一致しない場合がある。

df_s$regions

df_s$regions$confirmed[1]

## [1] 61089

df_s$regions$dailyConfirmedCount[[1]] %>% sum()

## [1] 67027

　

日次集計

個票データを日次で集計したもの。日付を見れば分かる通り暗黙の欠落を含んでいる。

df_s$daily

　

更新日時

集計データの更新日時。

df_s$updated

## [1] "2020-11-17T21:12:39+09:00"

　

Area Data

地域・地方ごとの分析を行う場合に便利な都道府県データを用意した。このデータはGistで公開している。

　

Others

病床データ

新型コロナウイルス対策病床オープンデータのデータも用意しておく。

if (googlesheets4::gs4_has_token()) {
beds_by_pref <- "https://docs.google.com/spreadsheets/d/1u0Ul8TgJDqoZMnqFrILyXzTHvuHMht1El7wDZeVrpp8" %>% 
  googlesheets4::read_sheet() %>% 
  dplyr::arrange(dplyr::desc(`発表日`)) %>% 
  dplyr::distinct(`自治体名`, .keep_all = TRUE) %>% 
  dplyr::rename(pref = `自治体名`, beds = `新型コロナウイルス対策感染症病床数`,
                date = `発表日`) %>% 
  dplyr::mutate(beds = as.integer(beds), date = lubridate::as_date(date))
beds_by_pref
}

COVID-19 対策ダッシュボード

NECソリューションイノベータによる都道府県単位の単日集計データ。治療に関する集計データが含まれている。ただし、項目によっては累積値（累計値）のものもある。

"https://covid-19.nec-solutioninnovators.com/download/japan_covid19.csv" %>% 
  readr::read_csv(guess_max = 12500) %>% 
  dplyr::select(date = `公表_年月日`, pref = `都道府県名`,
                confirmed = `PCR検査陽性者`, pcr = `PCR検査実施人数`,
                `入院治療等を要する者`, `うち重症`, `退院又は療養解除となった者の数`,
                `確認中`) %>% 
  dplyr::mutate(date = lubridate::as_date(date)) %>% 
  dplyr::mutate_if(is.numeric, .funs = as.integer)

　

Summarize

最初に個票データの内容を確認する。これには要約に便利なskimrパッケージを用いる。

df %>% 
  skimr::skim()

Data summary

Name	Piped data
Number of rows	123022
Number of columns	23
_______________________
Column type frequency:
character	19
logical	3
numeric	1
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
patientId	0	1.00	1	8	0	121248	0
dateAnnounced	0	1.00	10	10	0	294	0
gender	24922	0.80	1	1	0	2	0
detectedPrefecture	0	1.00	3	15	0	49	0
patientStatus	118958	0.03	8	23	0	8	0
notes	64794	0.47	1	270	0	55340	1
mhlwPatientNumber	122573	0.00	1	11	0	434	0
prefecturePatientNumber	21680	0.82	5	20	0	101333	0
prefectureSourceURL	91665	0.25	5	224	0	3451	0
residence	32850	0.73	1	38	0	1427	0
sourceURL	637	0.99	1	239	0	8588	0
relatedPatients	111779	0.09	2	259	0	6735	0
knownCluster	120521	0.02	3	88	0	231	0
detectedCityTown	95788	0.22	2	22	0	667	0
cityPrefectureNumber	96080	0.22	1	34	0	26933	2
citySourceURL	111071	0.10	9	317	0	3663	0
deceasedDate	121117	0.02	10	10	0	244	0
deceasedReportedDate	121802	0.01	10	62	0	207	0
deathSourceURL	121947	0.01	14	123	0	656	0

Variable type: logical

skim_variable	n_missing	complete_rate	mean	count
confirmedPatient	0	1	0.99	TRU: 121247, FAL: 1775
charterFlightPassenger	123008	0	1.00	TRU: 14
cruisePassengerDisembarked	123011	0	1.00	TRU: 11

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
ageBracket	0	1	30.06	24.3	-1	10	30	50	100	▆▇▅▂▁

　
元がJSON形式なので、読み込んだ直後は殆どの変量（フィーチャー）が文字型になっていることが分かる。また、意外と欠損が多いことも分かる。

　

Tidy & Transform

各変量（フィーチャー）を適切な形式に変換し、地域区分でも分析できるように都道府県データと結合することで、ベースとなるデータセットを作成する。なお、都道府県以外で報告されたレコードを除いている。

x <- df %>% 
  dplyr::select(patientId, date = dateAnnounced, gender,
                pref = detectedPrefecture, patientStatus, knownCluster,
                confirmedPatient, charterFlightPassenger,
                cruisePassengerDisembarked, ageBracket,
                deceasedDate, deceasedReportedDate) %>% 
  dplyr::filter(confirmedPatient == TRUE) %>% 
  dplyr::mutate(date = lubridate::as_date(date),
                gender = forcats::as_factor(gender),
                patientStatus = forcats::as_factor(patientStatus),
                cluster = dplyr::if_else(!is.na(knownCluster), TRUE, FALSE),
                ageBracket = forcats::as_factor(ageBracket),
                deceasedDate = lubridate::as_date(deceasedDate),
                deceasedReportedDate = lubridate::as_date(deceasedReportedDate)) %>% 
  dplyr::left_join(prefs, by = c("pref" = "pref")) %>% 
  dplyr::select(-`推計人口`, -pref) %>% 
  dplyr::rename(pref = `都道府県`, region = `八地方区分`) %>% 
  tidyr::drop_na(pref)

x

変換結果を要約してみると

x %>% 
  skimr::skim()

Data summary

Name	Piped data
Number of rows	119873
Number of columns	18
_______________________
Column type frequency:
character	2
Date	3
factor	9
logical	4
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
patientId	0	1.00	2	8	0	119873	0
knownCluster	117422	0.02	3	88	0	228	0

Variable type: Date

skim_variable	n_missing	complete_rate	min	max	median	n_unique
date	0	1	2020-01-15	2020-11-17	2020-08-21	291
deceasedDate	119495	0	2020-02-13	2020-10-17	2020-05-08	149
deceasedReportedDate	119544	0	2020-02-13	2020-10-17	2020-05-16	130

Variable type: factor

skim_variable	n_missing	complete_rate	ordered	n_unique	top_counts
gender	23364	0.81	FALSE	2	M: 54053, F: 42456
patientStatus	117363	0.02	FALSE	8	Hos: 1246, Dec: 370, Hom: 315, Dis: 276
ageBracket	0	1.00	FALSE	13	20: 26244, -1: 23450, 30: 16755, 40: 13943
pcode	0	1.00	FALSE	47	13: 35267, 27: 15763, 14: 10367, 23: 7913
pref	0	1.00	FALSE	47	東京都: 35267, 大阪府: 15763, 神奈川: 10367, 愛知県: 7913
region	0	1.00	FALSE	8	関東地: 61089, 近畿地: 24752, 中部地: 12238, 九州地: 11763
広域圏	9664	0.92	FALSE	8	首都圏: 61378, 近畿圏: 24125, 中部圏: 10779, 九州圏: 7968
通俗的区分	0	1.00	FALSE	11	関東: 61089, 関西: 24125, 東海: 10285, 九州: 7968
fct_pref	0	1.00	FALSE	47	Tok: 35267, Osa: 15763, Kan: 10367, Aic: 7913

Variable type: logical

skim_variable	n_missing	complete_rate	mean	count
confirmedPatient	0	1	1.00	TRU: 119873
charterFlightPassenger	119866	0	1.00	TRU: 7
cruisePassengerDisembarked	119862	0	1.00	TRU: 11
cluster	0	1	0.02	FAL: 117422, TRU: 2451

　
文字型を因子型に変換するだけでも大まかな傾向が見えるようになる。例えば

• 年齢別で見ると20代、30代、年齢不明（恐らく非回答）、40代の順に多い
• 都道府県別では東京、大阪、神奈川、愛知の順と人口にほぼ比例
• 地方区分で見ると関東、近畿、九州、中部となっており九州地方が以外と多い

ことが読める。

patientStatusは以下の通りで、ほぼ更新されていないのと思われる。死者数などの推移を見る場合は集計データを使った方がいいことが分かる。

x %>% 
  dplyr::group_by(patientStatus) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::ungroup() %>% 
  dplyr::mutate(Japanese = c("回復", "入院中", "退院済", "死亡", "詳細不明",
                             "重症", "自宅療養", "ホテル療養", NA))

　

Data Wrangling

陽性者の集計

最初に陽性者をキーに集計する。 　

全国集計

全国の累計陽性者数と推計人口[千人]、ならびに、人口千人あたりの累計陽性者数。

r_by_all <- x %>% 
  dplyr::filter(!is.na(pref)) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::bind_cols(prefs %>% dplyr::summarise(population = sum(`推計人口`))) %>% 
  dplyr::mutate(rate = round(n / population, 2))

r_by_all %>% 
  dplyr::rename(`累計陽性者数[人]` = n, `推計人口[千人]` = population,
                `人口千人あたりの累計陽性者数` = rate)

　

地方別集計

次に地方別の累計陽性者数と推計人口[千人]、ならびに、人口千人あたりの累計陽性者数。

region <- prefs %>% 
  dplyr::group_by(`八地方区分`) %>% 
  dplyr::summarise(population = sum(`推計人口`)) %>% 
  dplyr::rename(region = `八地方区分`)

r_by_region <- x %>% 
  dplyr::group_by(region) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  dplyr::left_join(region, by = c("region" = "region")) %>% 
  dplyr::select(region, n, population) %>% 
  dplyr::mutate(rate = round(n / population, 2))

r_by_region %>% 
  dplyr::rename(`地方` = region,
                `累計陽性者数[人]` = n, `推計人口[千人]` = population,
                `人口千人あたりの累計陽性者数` = rate)

　

上表を可視化する。グレーの破線は切片ゼロで傾きが全国の人口千人あたりの累計陽性者数（0.95）。

r_by_region %>% 
  dplyr::rename(key = region) %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = r_by_all$rate, intercept = 0,
                         colour = "gray", linetype = "dashed") + 
    ggplot2::geom_point(ggplot2::aes(colour = key)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = key, colour = key)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("推計人口と累計陽性者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数[人]")

　

都道府県別集計

同様に都道府県別の累計陽性者数と推計人口[千人]、ならびに、人口千人あたりの累計陽性者数。任意の列でソートできるようにしてある。

r_by_pref <- x %>% 
  dplyr::group_by(pref) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  dplyr::left_join(prefs, by = c("pref" = "都道府県")) %>% 
  dplyr::select(pref, n, population = `推計人口`) %>% 
  dplyr::mutate(rate = round(n / population, 2))

r_by_pref %>% 
  dplyr::rename(`都道府県` = pref,
                `累計陽性者数[人]` = n, `推計人口[千人]` = population,
                `人口千人あたりの累計陽性者数` = rate) %>% 
  tibble::rowid_to_column("No") %>% 
  DT::datatable()

　

上表を可視化する。グレーの破線は切片ゼロで傾きが全国の人口千人あたりの累計陽性者数（0.95）。

r_by_pref %>% 
  dplyr::rename(key = pref) %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = r_by_all$rate, intercept = 0,
                         colour = "gray", linetype = "dashed") + 
    ggplot2::geom_point(ggplot2::aes(colour = key)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = key, colour = key)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("推計人口と累計陽性者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数[人]")

　

推計人口が550万人未満の都道府県のみ抽出する。グレーの破線は上図と同様。

r_by_pref %>% 
  dplyr::filter(population < 5500) %>% 
  dplyr::rename(key = pref) %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = r_by_all$rate, intercept = 0,
                         colour = "gray", linetype = "dashed") + 
    ggplot2::geom_point(ggplot2::aes(colour = key)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = key, colour = key)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("推計人口と累計陽性者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数[人]")

　

クラスタ比率

全国のクラスタ比率

x %>% 
  dplyr::filter(!is.na(pref)) %>% 
  dplyr::group_by(cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::pivot_wider(names_from = cluster, values_from = n) %>%
  dplyr::mutate(ratio = (`TRUE` / (`TRUE` + `FALSE`) * 100) %>% round(1)) %>% 
  dplyr::rename(`非クラスタ感染者[人]` = `FALSE`, `クラスタ感染者[人]` = `TRUE`,
                `クラスタ比率[%]` = ratio)

地方別クラスタ比率

地方別の累計陽性者数の内、クラスタ感染と判定された人数の割合を求める。

x %>% 
  dplyr::group_by(region, cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  tidyr::pivot_wider(names_from = cluster, values_from = n) %>%
  dplyr::mutate(ratio = (`TRUE` / (`TRUE` + `FALSE`) * 100) %>% round(1)) %>% 
  dplyr::rename(`地方` = region,
                `非クラスタ感染者[人]` = `FALSE`, `クラスタ感染者[人]` = `TRUE`,
                `クラスタ比率[%]` = ratio)

　

都道府県別クラスタ比率

同様に都道府県別のクラスタ比率。任意の列でソートできるようにしてある。

x %>% 
  dplyr::group_by(pref, cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  tidyr::pivot_wider(names_from = cluster, values_from = n) %>%
  dplyr::mutate(ratio = (`TRUE` / (`TRUE` + `FALSE`) * 100) %>% round(1)) %>% 
  tidyr::replace_na(list(`TRUE` = 0L, ratio = 0.0)) %>% 
  dplyr::rename(`都道府県` = pref,
                `非クラスタ感染者[人]` = `FALSE`, `クラスタ感染者[人]` = `TRUE`,
                `クラスタ比率[%]` = ratio) %>% 
  tibble::rowid_to_column(var = "No") %>% 
  DT::datatable()

　

陽性者の日次集計

　

全国日次集計

全国の日次単位の陽性者数、前日差、累計、移動平均を求める。

x_by_all <- x %>% 
  dplyr::group_by(date) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::complete(date = seq.Date(from = min(date), to = max(date), by = "day"),
                  fill = list(n = 0L)) %>% 
  dplyr::mutate(diff = lagdiff(n), cum = cumsum(n), ma7 = ma7(n), ma28 = ma28(n))

x_by_all %>% 
  dplyr::select(`発表日` = date, `陽性者数` = n, `前日差` = diff,
                `累計陽性者数` = cum, `移動平均（7日）` = ma7)

　

上表を可視化する。

sec_scale <- 100

x_by_all %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n), stat = "identity", width = 1.0,
                      alpha = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7), linetype = "dashed",
                       colour = "dark green", size = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale),
                       colour = "dark green", size = 1.0) +
    ggplot2::labs(title = paste0("【全国】陽性者数の推移（単日） @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") +
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（破線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積陽性者数（折線）")
    )

x_by_all %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff), colour = "dark green", alpha = 0.5) + 
    ggplot2::labs(title = paste0("【全国】陽性者数の前日差 @", datetime),
                  subtitle = subtitle, caption = caption, 
                  x = "", y = "前日差")

　

地方別日次集計

同様に地方別の日次単位の陽性者数、前日差、累計、移動平均を求める。

x_by_region <- x %>% 
  dplyr::group_by(date, region) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::ungroup() %>% 
  tidyr::pivot_wider(names_from = region, values_from = n, values_fill = 0L) %>% 
  tidyr::complete(date = seq.Date(from = min(date), to = max(date), by = "day")) %>% 
  tidyr::pivot_longer(cols = -date, names_to = "region", values_to = "n") %>% 
  tidyr::replace_na(replace = list(n = 0L)) %>% 
  dplyr::group_by(region) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n)),
                ma28 = purrr::map(data, ~ ma28(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::left_join(prefs %>% dplyr::distinct(`八地方区分`), .,
                   by = c("八地方区分" = "region")) %>% 
  dplyr::mutate(region = forcats::fct_inorder(`八地方区分`)) %>% 
  dplyr::arrange(date)

x_by_region %>% 
  dplyr::filter(date == max(date)) %>% 
  dplyr::mutate(ma7 = round(ma7, 1)) %>% 
  dplyr::select(`地方` = region,
                `発表日` = date, `陽性者数` = n, `前日差` = diff,
                `陽性者累計` = cum, `移動平均（7日）` = ma7)

x_by_region %>% 
  dplyr::select(`地方` = region,
                `発表日` = date, `陽性者数` = n, `前日差` = diff,
                `陽性者累計` = cum, `移動平均（7日）` = ma7)

　

上表を可視化する。

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date, y = n)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = region), stat = "identity",
                      width = 1.0, alpha = 0.5) + 
    ggplot2::labs(title = paste0("【地方別】陽性者数の推移（単日） @", datetime),
                  subtitle = subtitle, caption = caption, 
                  x = "", y = "陽性者数") 

　

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date, y = ma7, colour = region)) + 
    ggplot2::geom_line(size = 1) +
    ggplot2::theme(legend.position = 'none') +
    ggplot2::labs(title = paste0("【地方別】移動平均（7日） @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "陽性者数") + 
    ggrepel::geom_text_repel(ggplot2::aes(label = region),
                             data = subset(x_by_region, date == max(date)),
                             nudge_x = 30, segment.alpha = 0.5, size = 4) + 
    ggplot2::lims(x = c(min(x_by_region$date),
                        max(x_by_region$date) + 45))

　

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date, y = cum, colour = region)) + 
    ggplot2::geom_line(size = 1) +
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("【地方別】累計陽性者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "累計陽性者数") + 
    ggrepel::geom_text_repel(ggplot2::aes(label = region),
                             data = subset(x_by_region, date == max(date)),
                             nudge_x = 30, segment.alpha = 0.5, size = 4) + 
    ggplot2::lims(x = c(min(x_by_region$date),
                        max(x_by_region$date) + 45))

　

地方単位で可視化。

sec_scale <- 20
ncol <- 2

x_by_region %>% 
  dplyr::rename(key = region) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
                      alpha = 0.5, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
                       linetype = "dotted", size = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
    ggplot2::facet_wrap(~ key, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（点線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "陽性者累計（実線）")
    )

　

傾向が見えるように縦軸をフリースケールとする。

sec_scale <- 20
ncol <- 2

x_by_region %>% 
  dplyr::rename(key = region) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
                      alpha = 0.5, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
                       linetype = "dashed", size = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
    ggplot2::facet_wrap(~ key, ncol  = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（破線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "陽性者累計（実線）")
    )

　

都道府県別日次集計

同様に都道府県別の日次単位の陽性者数、前日差、累計、移動平均を求める。

x_by_pref <- x %>% 
  dplyr::group_by(date, pref) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::ungroup() %>% 
  tidyr::pivot_wider(names_from = pref, values_from = n, values_fill = 0L) %>% 
  tidyr::complete(date = seq.Date(from = min(date), to = max(date), by = "day")) %>% 
  tidyr::pivot_longer(cols = -date, names_to = "pref", values_to = "n") %>% 
  tidyr::replace_na(replace = list(n = 0L)) %>% 
  dplyr::group_by(pref) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n)),
                ma28 = purrr::map(data, ~ ma28(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::left_join(prefs, ., by = c("都道府県" = "pref")) %>% 
  dplyr::mutate(pref = forcats::fct_inorder(`都道府県`)) %>% 
  dplyr::arrange(date)

x_by_pref %>% 
  dplyr::filter(date == max(date)) %>% 
  dplyr::mutate(ma7 = round(ma7, 1)) %>% 
  dplyr::select(`都道府県` = pref,
                `発表日` = date, `陽性者数` = n, `前日差` = diff,
                `陽性者累計` = cum, `移動平均（7日）` = ma7) %>% 
  DT::datatable()

x_by_pref %>% 
  dplyr::select(`都道府県` = pref,
                `発表日` = date, `陽性者数` = n, `前日差` = diff,
                `陽性者累計` = cum, `移動平均（7日）` = ma7)

　

上表を可視化する。

sec_scale <- 100
ncol <- 5
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")

x_by_pref %>% 
  dplyr::rename(key = pref) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
    ggplot2::facet_wrap(~ key, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累計陽性者数（折線）")
    )

　

傾向が見えるように縦軸をフリースケールとする。

x_by_pref %>% 
  dplyr::rename(key = pref) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
  ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
                      alpha = 0.35, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
    ggplot2::facet_wrap(~ key, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累計陽性者数（折線）")
    )

　

日次集計（死亡者）

　

都道府県別

都道府県別の日次単位の死亡者数、前日差、累計、移動平均（7日）を求める。

start <- df_s$prefectures %>% 
  dplyr::select(pref = name, date = dailyDeceasedStartDate) %>% 
  dplyr::left_join(prefs, by = c("pref" = "pref")) %>% 
  dplyr::arrange(pcode) %>% 
  tidyr::drop_na(pcode) %>% 
  dplyr::select(date, pref = `都道府県`) %>% 
  dplyr::distinct(date) %>% 
  .$date %>% lubridate::as_date()

d_by_prefs <- df_s$prefectures %>% 
  dplyr::select(deceased = dailyDeceasedCount, pref = name) %>% 
  dplyr::left_join(prefs, by = c("pref" = "pref")) %>% 
  tidyr::drop_na(pcode) %>% 
  dplyr::select(pref = `都道府県`, deceased) %>% 
  tidyr::unnest(deceased) %>% 
  tidyr::pivot_wider(names_from = pref, values_from = deceased) %>% 
  tidyr::unnest() %>% 
  dplyr::mutate(date = seq.Date(from = start, to = start + nrow(.) - 1,
                                by = "day")) %>% 
  dplyr::select(date, dplyr::everything()) %>% 
  tidyr::pivot_longer(col = -date, names_to = "pref", values_to = "n") %>% 
  dplyr::group_by(pref) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::left_join(prefs, ., by = c("都道府県" = "pref")) %>% 
  dplyr::mutate(pref = forcats::fct_inorder(`都道府県`)) %>% 
  dplyr::select(date, pref, n, diff, cum, ma7) %>% 
  dplyr::arrange(date)
d_by_prefs

sec_scale <- 100
ncol <- 5
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")

d_by_prefs %>% 
  dplyr::rename(key = pref) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
   ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
    ggplot2::facet_wrap(~ key, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累計陽性者数（折線）")
    )

　

地方別

集計データ$regionsには死亡者数の日次データが存在しないため$prefecturesのデータから計算する。

d_by_region <- d_by_prefs %>% 
  dplyr::select(date, pref = pref, n) %>% 
  dplyr::left_join(prefs, by = c("pref" = "都道府県")) %>% 
  tidyr::drop_na(pcode) %>% 
  dplyr::group_by(date, `八地方区分`) %>% 
  dplyr::summarise(n = sum(n)) %>% 
  dplyr::ungroup() %>% 
  dplyr::rename(region = `八地方区分`) %>% 
  dplyr::group_by(region) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::arrange(date)
d_by_region

　

陽性者比率と死亡者比率

rpd_by_all <- d_by_region %>% 
  dplyr::group_by(region) %>% 
  dplyr::summarise(d = sum(n)) %>% 
  dplyr::left_join(r_by_region, ., by = c("region")) %>% 
  dplyr::select(region, positive = n, deceased = d, population) %>% 
  dplyr::select(-region) %>% 
  dplyr::summarise_all(sum) %>% 
  dplyr::mutate(p_rate = round(positive / population, 2),
                d_rate = round(deceased / positive, 2))

rpd_by_all %>% 
  dplyr::rename(`陽性者数` = positive, `死亡者数` = deceased,
                `推計人口` = population, `人口千人あたりの陽性者比率` = p_rate,
                `陽性者に対する死亡者比率` = d_rate)

　

rpd_by_region <- d_by_region %>% 
  dplyr::group_by(region) %>% 
  dplyr::summarise(d = sum(n)) %>% 
  dplyr::left_join(r_by_region, ., by = c("region")) %>% 
  dplyr::select(region, positive = n, deceased = d, population, p_rate = rate) %>% 
  dplyr::mutate(d_rate = round(deceased / positive, 2))

rpd_by_region %>% 
  dplyr::rename(`陽性者数` = positive, `死亡者数` = deceased,
                `推計人口` = population, `人口千人あたりの陽性者比率` = p_rate,
                `陽性者に対する死亡者比率` = d_rate)

　

rpd_by_prefs <- d_by_prefs %>% 
  dplyr::group_by(pref) %>% 
  dplyr::summarise(d = sum(n)) %>% 
  dplyr::left_join(r_by_pref, ., by = "pref") %>% 
  dplyr::select(pref, positive = n, deceased = d, population, p_rate = rate) %>% 
  dplyr::mutate(d_rate = round(deceased / positive, 2)) 

rpd_by_prefs %>% 
  dplyr::rename(`陽性者数` = positive, `死亡者数` = deceased,
                `推計人口` = population, `人口千人あたりの陽性者比率` = p_rate,
                `陽性者に対する死亡者比率` = d_rate)

　

全国日次集計

都道府県別のデータから全国の日次集計を求める。

d_by_all <- d_by_prefs %>% 
  dplyr::group_by(date) %>% 
  dplyr::summarise(n = sum(n)) %>% 
  dplyr::ungroup() %>% 
  dplyr::mutate(diff = lagdiff(n), cum = cumsum(n), ma7 = ma7(n))
d_by_all

　

Visualize

前日差

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff, colour = region)) +
    ggplot2::facet_wrap(~ region, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数前日差, Free Y scale @", datetime),
                  caption = caption, x = "", y = "")

　

都道府県別

　

単日＋累計

sec_scale <- 100
ncol <- 5
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")


x_by_pref %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = pref), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = pref)) +
    ggplot2::facet_wrap(~ pref, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積陽性者数（折線）")
    )

x_by_pref %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
  ggplot2::geom_bar(ggplot2::aes(y = n, fill = pref), stat = "identity",
                      alpha = 0.35, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = pref)) +
    ggplot2::facet_wrap(~ pref, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積陽性者数（折線）")
    )

　

前日差

x_by_pref %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff, colour = pref)) +
    ggplot2::facet_wrap(~ pref, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数前日差, Free Y scale @", datetime),
                  x = "", y = "")

　

死亡者の日次推移

　

全国

sec_scale <- 100
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")

d_by_all %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n), stat = "identity", width = 1.0,
                      alpha = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7), linetype = "dashed",
                       colour = "dark green", size = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale),
                       colour = "dark green", size = 1.0) +
    ggplot2::labs(title = paste0("全国の死亡者数推移（単日） @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") +
    ggplot2::scale_y_continuous(
      name = "死亡者数・移動平均（破線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

d_by_all %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff), colour = "dark green", alpha = 0.5) + 
    ggplot2::labs(title = paste0("全国の死亡者数前日差 @", datetime),
                  subtitle = subtitle, caption = caption, 
                  x = "", y = "前日差")

　

地方別

sec_scale <- 50
ncol <- 4
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")


d_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = region), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = region),
                       linetype = "solid", size = 0.2) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = region)) +
    ggplot2::facet_wrap(~ region, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

d_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = region), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = region),
                       linetype = "solid", size = 0.2) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = region)) +
    ggplot2::facet_wrap(~ region, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

　

都道府県別日次推移

sec_scale <- 10
ncol <- 5
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")


d_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = pref), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = pref)) +
    ggplot2::facet_wrap(~ pref, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数（単日）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

d_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
  ggplot2::geom_bar(ggplot2::aes(y = n, fill = pref), stat = "identity",
                      alpha = 0.35, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = pref)) +
    ggplot2::facet_wrap(~ pref, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数（単日）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

　

比較

陽性者数と死亡者の比較。

　

全国

sec_scale <- (1 / 50)

x_by_all %>% 
  dplyr::left_join(d_by_all, by = c("date")) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n.x), stat = "identity",
                      fill = "dark green", alpha = 0.25, width = 1.0) +
    ggplot2::geom_bar(ggplot2::aes(y = n.y / sec_scale), stat = "identity",
                      fill = "dark red", alpha = 0.25, width = 1.0) +
    # ggplot2::geom_line(ggplot2::aes(y = n.x), colour = "dark green") + 
    # ggplot2::geom_line(ggplot2::aes(y = n.y / sec_scale), colour = "dark red") + 
    ggplot2::labs(title = paste0("@", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数（濃緑）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "死亡者数（濃赤）")
    )

　

地方別

sec_scale <- (1 / 10)
ncol <- 4

x_by_region %>% 
  dplyr::left_join(d_by_region, by = c("date" = "date", "region" = "region")) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n.x), stat = "identity",
                      fill = "dark green", alpha = 0.25, width = 1.0) +
    ggplot2::geom_bar(ggplot2::aes(y = n.y / sec_scale), stat = "identity",
                      fill = "dark red", alpha = 0.25, width = 1.0) +
    # ggplot2::geom_line(ggplot2::aes(y = n.x), colour = "dark green") + 
    # ggplot2::geom_line(ggplot2::aes(y = n.y / sec_scale), colour = "dark red") + 
    ggplot2::facet_wrap(~ region, ncol = ncol, scales = "free_y") + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数（濃緑）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "死亡者数（濃赤）")
    )

　

相関

　

地方区分別

r_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = 1, intercept = 0, colour = "gray") + 
    ggplot2::geom_point(ggplot2::aes(colour = region)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = region, colour = region)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("推計人口と陽性者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数")

　

rpd_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = positive, y = deceased)) + 
    ggplot2::geom_point(ggplot2::aes(colour = region)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = region, colour = region)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数と死亡者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "陽性者数", y = "死亡者数")

　

都道府県別

r_by_pref %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = 1, intercept = 0, colour = "gray") + 
    ggplot2::geom_point(ggplot2::aes(colour = pref)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = pref, colour = pref)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("@", datetime), caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数")

　

r_by_pref %>% 
  dplyr::filter(n < 5000) %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = 1, intercept = 0, colour = "gray") + 
    ggplot2::geom_point(ggplot2::aes(colour = pref)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = pref, colour = pref)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("累計陽性者数五千人未満 @", datetime),
                  caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数")

　

rpd_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = positive, y = deceased)) + 
    ggplot2::geom_abline(slope = rpd_by_all$d_rate, intercept = 0, colour = "gray") +
    ggplot2::geom_point(ggplot2::aes(colour = pref)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = pref, colour = pref)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数と死亡者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "陽性者数", y = "死亡者数")

　

rpd_by_prefs %>% 
  dplyr::filter(positive < 1000) %>% 
  ggplot2::ggplot(ggplot2::aes(x = positive, y = deceased)) + 
    ggplot2::geom_abline(slope = rpd_by_all$d_rate, intercept = 0, colour = "gray") +
    ggplot2::geom_point(ggplot2::aes(colour = pref)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = pref, colour = pref)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数と死亡者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "陽性者数", y = "死亡者数")

　

Model

時系列（TS）分析

日本の時系列データは週単位の変動が認められるので、frequencyを7に設定して陽性者数のデータをtsオブジェクトに変換する。

ts_week <- x_by_all %>% 
  dplyr::select(n) %>% 
  ts(frequency = 7)

時系列データに変換したものをプロットすると可視化の項でプロットした棒グラフと同じような形のグラフになることが分かります。

ts_week %>% 
  plot(main = paste0("全国 @", datetime))

上記からトレンド（長期的傾向）を除いたグラフ。デフォルト指定なのでlag = 1。つまり、前日差。

ts_week %>% 
    base::diff() %>% 
  plot(main = paste0("全国 @", datetime))

トレンド、季節変動（周期変動）、非周期変動に分解した場合。frequency = 1では分解できない点に注意。

ts_week %>% 
  stats::decompose() %>% 
  plot()

トレンドを抜き出してみる。移動平均に酷似している。

ts_week %>% 
  stats::decompose() %>% 
  .$x %>% 
  plot(ylim = c(0, 1500), main = paste0("全国 @", datetime))

par(new = TRUE)

ts_week %>% 
  stats::decompose() %>% 
  .$trend %>% 
  plot(ylim = c(0, 1500), col = "dark green", lwd = 3)

　

地方別時系列分析

x_by_region %>% 
  dplyr::select(region, n) %>% 
  split(.$region) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, name) {
                plot(.x, main = name)
              } )

## $北海道地方
## NULL
## 
## $東北地方
## NULL
## 
## $関東地方
## NULL
## 
## $中部地方
## NULL
## 
## $近畿地方
## NULL
## 
## $中国地方
## NULL
## 
## $四国地方
## NULL
## 
## $九州地方
## NULL

oldpar <- par()
par(mfrow=c(4, 2))
x_by_region %>% 
  dplyr::select(region, n) %>% 
  split(.$region) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, .y) {
                plot(.x, main = .y, ylim = c(0, max(.x)), col = "dark gray")
                par(new = TRUE)
                stats::decompose(.x) %>% 
                  .$trend %>% 
                  plot(ylim = c(0, max(.x)), col = "dark green", lwd = 2)
              } )

## $北海道地方
## NULL
## 
## $東北地方
## NULL
## 
## $関東地方
## NULL
## 
## $中部地方
## NULL
## 
## $近畿地方
## NULL
## 
## $中国地方
## NULL
## 
## $四国地方
## NULL
## 
## $九州地方
## NULL

par(oldpar)

x_by_pref %>% 
  dplyr::select(pref, n) %>% 
  split(.$pref) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, .y) {
                plot(.x, main = .y, ylim = c(0, max(.x)), col = "dark gray")
                # plot(.x, main = region)
                par(new = TRUE)
                stats::decompose(.x) %>% 
                  .$trend %>% 
                  plot(ylim = c(0, max(.x)), col = "dark green", lwd = 2)
              } )

## $北海道
## NULL
## 
## $青森県
## NULL
## 
## $岩手県
## NULL
## 
## $宮城県
## NULL
## 
## $秋田県
## NULL
## 
## $山形県
## NULL
## 
## $福島県
## NULL
## 
## $茨城県
## NULL
## 
## $栃木県
## NULL
## 
## $群馬県
## NULL
## 
## $埼玉県
## NULL
## 
## $千葉県
## NULL
## 
## $東京都
## NULL
## 
## $神奈川県
## NULL
## 
## $新潟県
## NULL
## 
## $富山県
## NULL
## 
## $石川県
## NULL
## 
## $福井県
## NULL
## 
## $山梨県
## NULL
## 
## $長野県
## NULL
## 
## $岐阜県
## NULL
## 
## $静岡県
## NULL
## 
## $愛知県
## NULL
## 
## $三重県
## NULL
## 
## $滋賀県
## NULL
## 
## $京都府
## NULL
## 
## $大阪府
## NULL
## 
## $兵庫県
## NULL
## 
## $奈良県
## NULL
## 
## $和歌山県
## NULL
## 
## $鳥取県
## NULL
## 
## $島根県
## NULL
## 
## $岡山県
## NULL
## 
## $広島県
## NULL
## 
## $山口県
## NULL
## 
## $徳島県
## NULL
## 
## $香川県
## NULL
## 
## $愛媛県
## NULL
## 
## $高知県
## NULL
## 
## $福岡県
## NULL
## 
## $佐賀県
## NULL
## 
## $長崎県
## NULL
## 
## $熊本県
## NULL
## 
## $大分県
## NULL
## 
## $宮崎県
## NULL
## 
## $鹿児島県
## NULL
## 
## $沖縄県
## NULL

　

Infer

時系列予測（ARIMA）

ARIMA（Auto Regressive Integrated Moving Average, 自己回帰和分移動平均）モデルによる陽性者に対する予測。予測に必要なパラメータはステップワイズにより自動的に最適なものが選択される。ただし、モデル自体を評価していないので、こういうことが出来る程度の話。

　

全国

x_by_all %>% 
  dplyr::select(n) %>% 
  ts(.$n, frequency = 7) %>% 
  forecast::auto.arima() %>%  
  forecast::forecast() %>% 
  plot(main = paste0("全国 @", datetime))

　

地方別

x_by_region %>% 
  dplyr::select(region, n) %>% 
  split(.$region) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map(., forecast::auto.arima) %>% 
  purrr::map(., forecast::forecast) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, name) {
                plot(.x, main = name)
              } )

## $北海道地方
## $北海道地方$mean
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##  [1] 206.9336 227.7285 232.6031 232.6111 232.3860 229.5111 236.6312 241.4335
##  [9] 249.0699 253.9556 258.5076 262.8575 267.1043 272.0663
## 
## $北海道地方$lower
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##               80%      95%
## 45.00000 196.1433 190.4312
## 45.14286 215.0053 208.2700
## 45.28571 216.1354 207.4179
## 45.42857 214.7482 205.2921
## 45.57143 212.9141 202.6063
## 45.71429 208.5487 197.4519
## 45.85714 214.1083 202.1853
## 46.00000 216.3030 202.9997
## 46.14286 221.8551 207.4485
## 46.28571 224.3283 208.6446
## 46.42857 226.8667 210.1171
## 46.57143 229.1349 211.2832
## 46.71429 231.3258 212.3858
## 46.85714 234.2002 214.1552
## 
## $北海道地方$upper
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##               80%      95%
## 45.00000 217.7240 223.4361
## 45.14286 240.4517 247.1870
## 45.28571 249.0707 257.7882
## 45.42857 250.4740 259.9301
## 45.57143 251.8580 262.1658
## 45.71429 250.4735 261.5703
## 45.85714 259.1542 271.0772
## 46.00000 266.5641 279.8674
## 46.14286 276.2847 290.6913
## 46.28571 283.5829 299.2667
## 46.42857 290.1484 306.8981
## 46.57143 296.5802 314.4319
## 46.71429 302.8829 321.8229
## 46.85714 309.9323 329.9774
## 
## 
## $東北地方
## $東北地方$mean
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##  [1] 27.26158 30.87598 27.59591 29.56287 29.60702 28.46690 30.17624 29.06402
##  [9] 29.58151 29.97403 29.39482 30.14500 29.86988 29.98287
## 
## $東北地方$lower
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##               80%      95%
## 45.00000 20.43937 16.82791
## 45.14286 23.55439 19.67857
## 45.28571 20.14799 16.20530
## 45.42857 22.09010 18.13426
## 45.57143 21.73540 17.56842
## 45.71429 20.57132 16.39166
## 45.85714 22.10030 17.82515
## 46.00000 20.83038 16.47176
## 46.14286 21.29074 16.90187
## 46.28571 21.48048 16.98426
## 46.42857 20.82146 16.28300
## 46.57143 21.45361 16.85266
## 46.71429 21.03870 16.36375
## 46.85714 21.06940 16.35090
## 
## $東北地方$upper
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##               80%      95%
## 45.00000 34.08378 37.69524
## 45.14286 38.19757 42.07339
## 45.28571 35.04384 38.98653
## 45.42857 37.03564 40.99148
## 45.57143 37.47864 41.64563
## 45.71429 36.36248 40.54214
## 45.85714 38.25219 42.52733
## 46.00000 37.29766 41.65628
## 46.14286 37.87228 42.26115
## 46.28571 38.46758 42.96380
## 46.42857 37.96818 42.50664
## 46.57143 38.83640 43.43734
## 46.71429 38.70106 43.37601
## 46.85714 38.89633 43.61484
## 
## 
## $関東地方
## $関東地方$mean
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##  [1] 689.7468 784.9956 748.2743 760.0752 581.5708 483.3957 664.2027 699.5433
##  [9] 790.6962 769.9503 779.5429 607.8589 499.7453 680.8631
## 
## $関東地方$lower
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##               80%      95%
## 45.00000 626.5432 593.0852
## 45.14286 705.5564 663.5038
## 45.28571 660.6234 614.2238
## 45.42857 669.2719 621.2036
## 45.57143 487.3104 437.4119
## 45.71429 385.0969 333.0606
## 45.85714 559.1164 503.4872
## 46.00000 581.5880 519.1462
## 46.14286 662.2695 594.2845
## 46.28571 634.9123 563.4276
## 46.42857 640.0445 566.1985
## 46.57143 464.9987 389.3731
## 46.71429 353.2571 275.7108
## 46.85714 530.2522 450.5236
## 
## $関東地方$upper
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##               80%      95%
## 45.00000 752.9503 786.4083
## 45.14286 864.4348 906.4874
## 45.28571 835.9253 882.3249
## 45.42857 850.8785 898.9469
## 45.57143 675.8313 725.7297
## 45.71429 581.6946 633.7309
## 45.85714 769.2889 824.9182
## 46.00000 817.4986 879.9404
## 46.14286 919.1230 987.1080
## 46.28571 904.9883 976.4730
## 46.42857 919.0413 992.8873
## 46.57143 750.7191 826.3447
## 46.71429 646.2336 723.7798
## 46.85714 831.4740 911.2026
## 
## 
## $中部地方
## $中部地方$mean
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##  [1] 206.6443 222.7549 248.0006 244.1964 216.3111 188.7725 258.5539 260.4424
##  [9] 269.0705 275.4434 275.2537 243.6957 225.2850 278.8496
## 
## $中部地方$lower
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##               80%      95%
## 45.00000 187.0452 176.6701
## 45.14286 197.6961 184.4308
## 45.28571 217.6343 201.5594
## 45.42857 208.6988 189.9075
## 45.57143 177.6514 157.1862
## 45.71429 145.8613 123.1455
## 45.85714 212.5617 188.2150
## 46.00000 207.4848 179.4508
## 46.14286 210.1072 178.8939
## 46.28571 211.6780 177.9226
## 46.42857 206.0455 169.4088
## 46.57143 170.2510 131.3718
## 46.71429 147.4970 106.3185
## 46.85714 196.7897 153.3498
## 
## $中部地方$upper
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##               80%      95%
## 45.00000 226.2434 236.6186
## 45.14286 247.8138 261.0791
## 45.28571 278.3668 294.4418
## 45.42857 279.6939 298.4852
## 45.57143 254.9707 275.4359
## 45.71429 231.6837 254.3995
## 45.85714 304.5461 328.8929
## 46.00000 313.3999 341.4340
## 46.14286 328.0338 359.2471
## 46.28571 339.2088 372.9642
## 46.42857 344.4620 381.0987
## 46.57143 317.1404 356.0197
## 46.71429 303.0730 344.2515
## 46.85714 360.9095 404.3494
## 
## 
## $近畿地方
## $近畿地方$mean
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##  [1] 455.8946 436.2468 477.4792 499.2772 458.7444 305.2802 522.0886 540.9577
##  [9] 525.4624 555.2106 579.5988 546.2210 393.3510 597.9506
## 
## $近畿地方$lower
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##               80%      95%
## 45.00000 420.1302 401.1977
## 45.14286 395.6737 374.1956
## 45.28571 434.8540 412.2895
## 45.42857 453.5605 429.3595
## 45.57143 410.1323 384.3986
## 45.71429 253.9358 226.7556
## 45.85714 468.1500 439.5967
## 46.00000 476.1793 441.8877
## 46.14286 455.3243 418.1955
## 46.28571 481.2102 442.0368
## 46.42857 501.3391 459.9109
## 46.57143 463.9222 420.3558
## 46.71429 307.2022 261.5977
## 46.85714 508.1166 460.5614
## 
## $近畿地方$upper
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##               80%      95%
## 45.00000 491.6589 510.5914
## 45.14286 476.8199 498.2980
## 45.28571 520.1044 542.6688
## 45.42857 544.9940 569.1950
## 45.57143 507.3565 533.0902
## 45.71429 356.6246 383.8047
## 45.85714 576.0271 604.5804
## 46.00000 605.7362 640.0278
## 46.14286 595.6005 632.7293
## 46.28571 629.2109 668.3844
## 46.42857 657.8584 699.2866
## 46.57143 628.5199 672.0862
## 46.71429 479.4998 525.1043
## 46.85714 687.7846 735.3398
## 
## 
## $中国地方
## $中国地方$mean
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##  [1] 19.50669 19.01175 19.01175 19.01175 19.01175 19.01175 19.01175 19.01175
##  [9] 19.01175 19.01175 19.01175 19.01175 19.01175 19.01175
## 
## $中国地方$lower
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##                80%      95%
## 45.00000 10.348923 5.501093
## 45.14286  9.426359 4.352159
## 45.28571  9.227695 4.048328
## 45.42857  9.032985 3.750545
## 45.57143  8.842002 3.458462
## 45.71429  8.654540 3.171763
## 45.85714  8.470411 2.890163
## 46.00000  8.289444 2.613398
## 46.14286  8.111481 2.341226
## 46.28571  7.936377 2.073428
## 46.42857  7.763999 1.809798
## 46.57143  7.594223 1.550148
## 46.71429  7.426934 1.294303
## 46.85714  7.262028 1.042100
## 
## $中国地方$upper
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##               80%      95%
## 45.00000 28.66446 33.51229
## 45.14286 28.59714 33.67134
## 45.28571 28.79581 33.97517
## 45.42857 28.99052 34.27296
## 45.57143 29.18150 34.56504
## 45.71429 29.36896 34.85174
## 45.85714 29.55309 35.13334
## 46.00000 29.73406 35.41010
## 46.14286 29.91202 35.68228
## 46.28571 30.08713 35.95007
## 46.42857 30.25950 36.21370
## 46.57143 30.42928 36.47335
## 46.71429 30.59657 36.72920
## 46.85714 30.76147 36.98140
## 
## 
## $四国地方
## $四国地方$mean
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##  [1] 6.742293 5.676504 5.676504 5.676504 5.676504 5.676504 5.676504 5.676504
##  [9] 5.676504 5.676504 5.676504 5.676504 5.676504 5.676504
## 
## $四国地方$lower
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##               80%        95%
## 45.00000 3.223747  1.3611414
## 45.14286 1.882173 -0.1264245
## 45.28571 1.798556 -0.2543055
## 45.42857 1.716705 -0.3794867
## 45.57143 1.636511 -0.5021322
## 45.71429 1.557879 -0.6223901
## 45.85714 1.480720 -0.7403946
## 46.00000 1.404954 -0.8562680
## 46.14286 1.330509 -0.9701216
## 46.28571 1.257318 -1.0820575
## 46.42857 1.185320 -1.1921695
## 46.57143 1.114458 -1.3005439
## 46.71429 1.044680 -1.4072604
## 46.85714 0.975937 -1.5123930
## 
## $四国地方$upper
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##                80%      95%
## 45.00000 10.260838 12.12344
## 45.14286  9.470836 11.47943
## 45.28571  9.554452 11.60731
## 45.42857  9.636304 11.73250
## 45.57143  9.716498 11.85514
## 45.71429  9.795130 11.97540
## 45.85714  9.872289 12.09340
## 46.00000  9.948055 12.20928
## 46.14286 10.022499 12.32313
## 46.28571 10.095690 12.43507
## 46.42857 10.167689 12.54518
## 46.57143 10.238551 12.65355
## 46.71429 10.308329 12.76027
## 46.85714 10.377072 12.86540
## 
## 
## $九州地方
## $九州地方$mean
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##  [1] 72.98561 68.60518 80.24047 63.23062 75.13570 61.19635 71.95182 75.35949
##  [9] 72.32891 81.90916 71.18658 77.57866 67.81072 75.85945
## 
## $九州地方$lower
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##                80%           95%
## 45.00000 51.083036  39.488512722
## 45.14286 42.093206  28.058613747
## 45.28571 50.228871  34.341690697
## 45.42857 31.909367  15.328895015
## 45.57143 41.724416  24.037549135
## 45.71429 23.912314   4.175337962
## 45.85714 32.084565  10.980115993
## 46.00000 29.980523   5.958350484
## 46.14286 23.098746  -2.962128457
## 46.28571 28.345322  -0.009661343
## 46.42857 14.473324 -15.548859896
## 46.57143 18.063484 -13.441945289
## 46.71429  4.996164 -28.255850647
## 46.85714  9.869112 -25.064061323
## 
## $九州地方$upper
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##                80%      95%
## 45.00000  94.88819 106.4827
## 45.14286  95.11716 109.1517
## 45.28571 110.25207 126.1392
## 45.42857  94.55188 111.1324
## 45.57143 108.54699 126.2339
## 45.71429  98.48039 118.2174
## 45.85714 111.81907 132.9235
## 46.00000 120.73845 144.7606
## 46.14286 121.55907 147.6199
## 46.28571 135.47300 163.8280
## 46.42857 127.89983 157.9220
## 46.57143 137.09383 168.5993
## 46.71429 130.62527 163.8773
## 46.85714 141.84978 176.7830

　

都道府県別

x_by_pref %>% 
  dplyr::select(pref, n) %>% 
  split(.$pref) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map(., forecast::auto.arima) %>% 
  purrr::map(., forecast::forecast) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, name) {
                plot(.x, main = name)
              } )

## $北海道
## $北海道$mean
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##  [1] 206.9336 227.7285 232.6031 232.6111 232.3860 229.5111 236.6312 241.4335
##  [9] 249.0699 253.9556 258.5076 262.8575 267.1043 272.0663
## 
## $北海道$lower
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##               80%      95%
## 45.00000 196.1433 190.4312
## 45.14286 215.0053 208.2700
## 45.28571 216.1354 207.4179
## 45.42857 214.7482 205.2921
## 45.57143 212.9141 202.6063
## 45.71429 208.5487 197.4519
## 45.85714 214.1083 202.1853
## 46.00000 216.3030 202.9997
## 46.14286 221.8551 207.4485
## 46.28571 224.3283 208.6446
## 46.42857 226.8667 210.1171
## 46.57143 229.1349 211.2832
## 46.71429 231.3258 212.3858
## 46.85714 234.2002 214.1552
## 
## $北海道$upper
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##               80%      95%
## 45.00000 217.7240 223.4361
## 45.14286 240.4517 247.1870
## 45.28571 249.0707 257.7882
## 45.42857 250.4740 259.9301
## 45.57143 251.8580 262.1658
## 45.71429 250.4735 261.5703
## 45.85714 259.1542 271.0772
## 46.00000 266.5641 279.8674
## 46.14286 276.2847 290.6913
## 46.28571 283.5829 299.2667
## 46.42857 290.1484 306.8981
## 46.57143 296.5802 314.4319
## 46.71429 302.8829 321.8229
## 46.85714 309.9323 329.9774
## 
## 
## $青森県
## $青森県$mean
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##  [1] 0.1150807 1.1970481 0.5883618 0.9307926 0.7381501 0.8465257 0.7855564
##  [8] 0.8198561 0.8005600 0.8114155 0.8053085 0.8087441 0.8068113 0.8078987
## 
## $青森県$lower
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##                80%       95%
## 45.00000 -2.433183 -3.782153
## 45.14286 -1.793033 -3.375886
## 45.28571 -2.481753 -4.106974
## 45.42857 -2.359954 -4.101971
## 45.57143 -2.676101 -4.483496
## 45.71429 -2.729827 -4.623033
## 45.85714 -2.922000 -4.884661
## 46.00000 -3.027175 -5.063670
## 46.14286 -3.174060 -5.278097
## 46.28571 -3.290623 -5.462112
## 46.42857 -3.418227 -5.654031
## 46.57143 -3.534026 -5.832950
## 46.71429 -3.651383 -6.011409
## 46.85714 -3.763148 -6.182914
## 
## $青森県$upper
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##               80%      95%
## 45.00000 2.663345 4.012314
## 45.14286 4.187129 5.769982
## 45.28571 3.658477 5.283698
## 45.42857 4.221540 5.963556
## 45.57143 4.152401 5.959796
## 45.71429 4.422878 6.316085
## 45.85714 4.493112 6.455774
## 46.00000 4.666887 6.703383
## 46.14286 4.775180 6.879217
## 46.28571 4.913454 7.084943
## 46.42857 5.028844 7.264648
## 46.57143 5.151515 7.450438
## 46.71429 5.265006 7.625031
## 46.85714 5.378945 7.798711
## 
## 
## $岩手県
## $岩手県$mean
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##  [1] 2.6274604 3.3539089 3.3861709 2.4603133 2.5105547 1.7919075 1.6793853
##  [8] 0.8139243 0.6027999 0.2233497 1.2273886 1.0895068 1.6231734 1.6011634
## 
## $岩手県$lower
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##                 80%        95%
## 45.00000  1.5084563  0.9160913
## 45.14286  1.9690277  1.2359159
## 45.28571  1.6714279  0.7636978
## 45.42857  0.5304791 -0.4911134
## 45.57143  0.3522664 -0.7902624
## 45.71429 -0.5520217 -1.7928228
## 45.85714 -0.8489350 -2.1873469
## 46.00000 -1.9449216 -3.4053665
## 46.14286 -2.3562012 -3.9226018
## 46.28571 -2.9306612 -4.6002937
## 46.42857 -2.1056402 -3.8700391
## 46.57143 -2.4162274 -4.2720510
## 46.71429 -2.0454084 -3.9874384
## 46.85714 -2.2244127 -4.2495503
## 
## $岩手県$upper
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##               80%      95%
## 45.00000 3.746464 4.338829
## 45.14286 4.738790 5.471902
## 45.28571 5.100914 6.008644
## 45.42857 4.390148 5.411740
## 45.57143 4.668843 5.811372
## 45.71429 4.135837 5.376638
## 45.85714 4.207706 5.546118
## 46.00000 3.572770 5.033215
## 46.14286 3.561801 5.128202
## 46.28571 3.377361 5.046993
## 46.42857 4.560417 6.324816
## 46.57143 4.595241 6.451065
## 46.71429 5.291755 7.233785
## 46.85714 5.426739 7.451877
## 
## 
## $宮城県
## $宮城県$mean
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##  [1] 15.75165 20.13049 15.39158 19.72555 16.26262 18.65623 17.32038 17.75154
##  [9] 17.99649 17.32410 18.19568 17.30791 18.08487 17.49013
## 
## $宮城県$lower
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##                80%       95%
## 45.00000 10.635033  7.926458
## 45.14286 14.912988 12.151009
## 45.28571  9.937447  7.050206
## 45.42857 14.242796 11.340399
## 45.57143 10.503543  7.454875
## 45.71429 12.865313  9.799789
## 45.85714 11.319938  8.143496
## 46.00000 11.688509  8.478932
## 46.14286 11.796426  8.514309
## 46.28571 11.024489  7.689674
## 46.42857 11.800167  8.414586
## 46.57143 10.792264  7.343090
## 46.71429 11.487641  7.995281
## 46.85714 10.772990  7.217153
## 
## $宮城県$upper
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##               80%      95%
## 45.00000 20.86827 23.57685
## 45.14286 25.34799 28.10997
## 45.28571 20.84571 23.73295
## 45.42857 25.20831 28.11071
## 45.57143 22.02169 25.07036
## 45.71429 24.44714 27.51267
## 45.85714 23.32082 26.49726
## 46.00000 23.81458 27.02416
## 46.14286 24.19656 27.47868
## 46.28571 23.62372 26.95853
## 46.42857 24.59120 27.97678
## 46.57143 23.82355 27.27273
## 46.71429 24.68209 28.17445
## 46.85714 24.20726 27.76310
## 
## 
## $秋田県
## $秋田県$mean
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##  [1] 0.5675663 0.5181793 0.5181793 0.5181793 0.5181793 0.5181793 0.5181793
##  [8] 0.5181793 0.5181793 0.5181793 0.5181793 0.5181793 0.5181793 0.5181793
## 
## $秋田県$lower
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##                 80%       95%
## 45.00000 -0.6658521 -1.318784
## 45.14286 -0.7272939 -1.386608
## 45.28571 -0.7290299 -1.389262
## 45.42857 -0.7307634 -1.391914
## 45.57143 -0.7324946 -1.394561
## 45.71429 -0.7342234 -1.397205
## 45.85714 -0.7359498 -1.399846
## 46.00000 -0.7376738 -1.402482
## 46.14286 -0.7393955 -1.405115
## 46.28571 -0.7411148 -1.407745
## 46.42857 -0.7428318 -1.410371
## 46.57143 -0.7445464 -1.412993
## 46.71429 -0.7462587 -1.415612
## 46.85714 -0.7479687 -1.418227
## 
## $秋田県$upper
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##               80%      95%
## 45.00000 1.800985 2.453917
## 45.14286 1.763652 2.422966
## 45.28571 1.765388 2.425621
## 45.42857 1.767122 2.428272
## 45.57143 1.768853 2.430920
## 45.71429 1.770582 2.433564
## 45.85714 1.772308 2.436204
## 46.00000 1.774032 2.438841
## 46.14286 1.775754 2.441474
## 46.28571 1.777473 2.444103
## 46.42857 1.779190 2.446729
## 46.57143 1.780905 2.449352
## 46.71429 1.782617 2.451970
## 46.85714 1.784327 2.454585
## 
## 
## $山形県
## $山形県$mean
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##  [1] 0.5544428 0.6499650 0.9568427 0.4580574 0.5307093 0.4458003 0.5769197
##  [8] 0.3163039 0.2783836 0.2470521 0.2218462 0.2022642 0.1877808 0.1778604
## 
## $山形県$lower
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##                 80%        95%
## 45.00000 -0.3938721 -0.8958797
## 45.14286 -0.3192851 -0.8323751
## 45.28571 -0.0419985 -0.5707531
## 45.42857 -0.5770221 -1.1249600
## 45.57143 -0.5447673 -1.1140903
## 45.71429 -0.6718116 -1.2634395
## 45.85714 -0.5825085 -1.1962728
## 46.00000 -0.8488405 -1.4656306
## 46.14286 -0.9152713 -1.5471541
## 46.28571 -0.9704268 -1.6149212
## 46.42857 -1.0151272 -1.6699414
## 46.57143 -1.0503166 -1.7133928
## 46.71429 -1.0770073 -1.7465457
## 46.85714 -1.0962345 -1.7706996
## 
## $山形県$upper
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##               80%      95%
## 45.00000 1.502758 2.004765
## 45.14286 1.619215 2.132305
## 45.28571 1.955684 2.484438
## 45.42857 1.493137 2.041075
## 45.57143 1.606186 2.175509
## 45.71429 1.563412 2.155040
## 45.85714 1.736348 2.350112
## 46.00000 1.481448 2.098238
## 46.14286 1.472039 2.103921
## 46.28571 1.464531 2.109025
## 46.42857 1.458820 2.113634
## 46.57143 1.454845 2.117921
## 46.71429 1.452569 2.122107
## 46.85714 1.451955 2.126420
## 
## 
## $福島県
## $福島県$mean
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##  [1] 3.156745 3.156745 3.156745 3.156745 3.156745 3.156745 3.156745 3.156745
##  [9] 3.156745 3.156745 3.156745 3.156745 3.156745 3.156745
## 
## $福島県$lower
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##                   80%        95%
## 45.00000  0.621276781 -0.7209188
## 45.14286  0.558275333 -0.8172712
## 45.28571  0.496765652 -0.9113422
## 45.42857  0.436646537 -1.0032864
## 45.57143  0.377827735 -1.0932420
## 45.71429  0.320228354 -1.1813327
## 45.85714  0.263775557 -1.2676698
## 46.00000  0.208403481 -1.3523541
## 46.14286  0.154052337 -1.4354770
## 46.28571  0.100667655 -1.5171218
## 46.42857  0.048199640 -1.5973647
## 46.57143 -0.003397365 -1.6762755
## 46.71429 -0.054165350 -1.7539184
## 46.85714 -0.104143037 -1.8303527
## 
## $福島県$upper
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##               80%      95%
## 45.00000 5.692213 7.034408
## 45.14286 5.755214 7.130761
## 45.28571 5.816724 7.224831
## 45.42857 5.876843 7.316776
## 45.57143 5.935662 7.406731
## 45.71429 5.993261 7.494822
## 45.85714 6.049714 7.581159
## 46.00000 6.105086 7.665843
## 46.14286 6.159437 7.748966
## 46.28571 6.212822 7.830611
## 46.42857 6.265290 7.910854
## 46.57143 6.316887 7.989765
## 46.71429 6.367655 8.067408
## 46.85714 6.417632 8.143842
## 
## 
## $茨城県
## $茨城県$mean
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##  [1] 39.95466 37.06146 40.13233 42.47158 44.25352 45.61091 46.64491 47.43256
##  [9] 48.03256 48.48961 48.83778 49.10299 49.30501 49.45891
## 
## $茨城県$lower
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##               80%      95%
## 45.00000 35.20800 32.69527
## 45.14286 32.05853 29.41014
## 45.28571 35.00947 32.29758
## 45.42857 37.10172 34.25908
## 45.57143 38.52443 35.49163
## 45.71429 39.44057 36.17419
## 45.85714 39.98136 36.45389
## 46.00000 40.24769 36.44425
## 46.14286 40.31513 36.22977
## 46.28571 40.23951 35.87217
## 46.42857 40.06189 35.41622
## 46.57143 39.81235 34.89419
## 46.71429 39.51292 34.32930
## 46.85714 39.17970 33.73821
## 
## $茨城県$upper
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##               80%      95%
## 45.00000 44.70132 47.21405
## 45.14286 42.06439 44.71278
## 45.28571 45.25520 47.96708
## 45.42857 47.84145 50.68409
## 45.57143 49.98260 53.01540
## 45.71429 51.78125 55.04763
## 45.85714 53.30846 56.83593
## 46.00000 54.61744 58.42088
## 46.14286 55.75000 59.83536
## 46.28571 56.73972 61.10705
## 46.42857 57.61366 62.25933
## 46.57143 58.39362 63.31179
## 46.71429 59.09711 64.28073
## 46.85714 59.73812 65.17961
## 
## 
## $栃木県
## $栃木県$mean
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##  [1] 2.450115 3.252504 2.480106 3.081372 2.754139 2.261486 3.236368 2.498852
##  [9] 2.676619 2.505479 2.534360 2.493454 2.496883 2.486770
## 
## $栃木県$lower
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##                  80%       95%
## 45.00000 -0.47341307 -2.021035
## 45.14286  0.29415020 -1.271908
## 45.28571 -0.63032699 -2.276891
## 45.42857 -0.05205188 -1.710786
## 45.57143 -0.41178956 -2.087731
## 45.71429 -0.91968734 -2.603699
## 45.85714  0.03951689 -1.652794
## 46.00000 -0.84144187 -2.609687
## 46.14286 -0.68578493 -2.465734
## 46.28571 -0.89319467 -2.692344
## 46.42857 -0.88300078 -2.692042
## 46.57143 -0.94406140 -2.763772
## 46.71429 -0.95719180 -2.785668
## 46.85714 -0.98383430 -2.821061
## 
## $栃木県$upper
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##               80%      95%
## 45.00000 5.373643 6.921265
## 45.14286 6.210858 7.776916
## 45.28571 5.590540 7.237104
## 45.42857 6.214797 7.873531
## 45.57143 5.920067 7.596008
## 45.71429 5.442658 7.126670
## 45.85714 6.433219 8.125529
## 46.00000 5.839147 7.607392
## 46.14286 6.039024 7.818973
## 46.28571 5.904152 7.703301
## 46.42857 5.951720 7.760761
## 46.57143 5.930969 7.750680
## 46.71429 5.950957 7.779434
## 46.85714 5.957375 7.794602
## 
## 
## $群馬県
## $群馬県$mean
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##  [1] 1.554772 5.152706 4.001320 4.108765 4.379284 4.492357 4.642283 4.729306
##  [9] 4.818175 4.878859 4.933696 4.974320 5.008912 5.035604
## 
## $群馬県$lower
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##                  80%       95%
## 45.00000 -2.91073329 -5.274629
## 45.14286  0.02325912 -2.692106
## 45.28571 -1.31795954 -4.133816
## 45.42857 -1.26725277 -4.113144
## 45.57143 -1.36713901 -4.409112
## 45.71429 -1.34059858 -4.428379
## 45.85714 -1.33951050 -4.506081
## 46.00000 -1.32513195 -4.530158
## 46.14286 -1.31670785 -4.564319
## 46.28571 -1.31130093 -4.588174
## 46.42857 -1.30941592 -4.614320
## 46.57143 -1.31168294 -4.639292
## 46.71429 -1.31684957 -4.665505
## 46.85714 -1.32517683 -4.692371
## 
## $群馬県$upper
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##                80%       95%
## 45.00000  6.020278  8.384174
## 45.14286 10.282154 12.997519
## 45.28571  9.320599 12.136456
## 45.42857  9.484782 12.330673
## 45.57143 10.125708 13.167680
## 45.71429 10.325312 13.413092
## 45.85714 10.624076 13.790646
## 46.00000 10.783744 13.988769
## 46.14286 10.953059 14.200670
## 46.28571 11.069019 14.345892
## 46.42857 11.176808 14.481712
## 46.57143 11.260324 14.587933
## 46.71429 11.334673 14.683329
## 46.85714 11.396386 14.763580
## 
## 
## $埼玉県
## $埼玉県$mean
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##  [1] 105.16858  90.97717 100.57920 105.07560  94.16563  98.05927  99.83723
##  [8] 110.22762 101.63265  96.57910 105.32997 101.39928 103.16523 101.84293
## 
## $埼玉県$lower
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##               80%      95%
## 45.00000 91.27591 83.92157
## 45.14286 76.44670 68.75474
## 45.28571 85.43777 77.42239
## 45.42857 89.34693 81.02068
## 45.57143 77.87087 69.24495
## 45.71429 81.21744 72.30192
## 45.85714 82.46554 73.26953
## 46.00000 90.70214 80.36598
## 46.14286 81.29642 70.53107
## 46.28571 75.46322 64.28515
## 46.42857 83.46222 71.88613
## 46.57143 78.80467 66.84380
## 46.71429 79.86642 67.53278
## 46.85714 77.86059 65.16510
## 
## $埼玉県$upper
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##               80%      95%
## 45.00000 119.0613 126.4156
## 45.14286 105.5076 113.1996
## 45.28571 115.7206 123.7360
## 45.42857 120.8043 129.1305
## 45.57143 110.4604 119.0863
## 45.71429 114.9011 123.8166
## 45.85714 117.2089 126.4049
## 46.00000 129.7531 140.0893
## 46.14286 121.9689 132.7342
## 46.28571 117.6950 128.8731
## 46.42857 127.1977 138.7738
## 46.57143 123.9939 135.9548
## 46.71429 126.4640 138.7977
## 46.85714 125.8253 138.5208
## 
## 
## $千葉県
## $千葉県$mean
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##  [1] 76.76768 79.64910 75.65223 83.43014 76.05728 79.62916 80.38326 81.29553
##  [9] 81.25867 80.17964 82.88927 80.10359 82.47038 82.19216
## 
## $千葉県$lower
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##               80%      95%
## 45.00000 65.04104 58.83334
## 45.14286 66.58457 59.66862
## 45.28571 62.04294 54.83862
## 45.42857 69.29707 61.81547
## 45.57143 61.41916 53.67021
## 45.71429 64.50285 56.49546
## 45.85714 64.78402 56.52628
## 46.00000 64.32203 55.33680
## 46.14286 63.49896 54.09754
## 46.28571 61.79628 52.06472
## 46.42857 63.90274 53.85189
## 46.57143 60.53248 50.17216
## 46.71429 62.33163 51.67083
## 46.85714 61.50136 50.54831
## 
## $千葉県$upper
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##                80%       95%
## 45.00000  88.49432  94.70202
## 45.14286  92.71363  99.62958
## 45.28571  89.26153  96.46585
## 45.42857  97.56321 105.04480
## 45.57143  90.69540  98.44435
## 45.71429  94.75548 102.76286
## 45.85714  95.98249 104.24023
## 46.00000  98.26902 107.25425
## 46.14286  99.01838 108.41980
## 46.28571  98.56299 108.29455
## 46.42857 101.87579 111.92665
## 46.57143  99.67470 110.03502
## 46.71429 102.60912 113.26992
## 46.85714 102.88296 113.83601
## 
## 
## $東京都
## $東京都$mean
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##  [1] 290.1421 374.0015 346.3385 358.7400 258.4438 205.6632 312.3812 300.2231
##  [9] 383.7738 355.7731 367.8487 267.2378 214.1535 320.5781
## 
## $東京都$lower
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##               80%       95%
## 45.00000 244.7335 220.69567
## 45.14286 321.5191 293.73660
## 45.28571 290.6753 261.20894
## 45.42857 300.2676 269.31415
## 45.57143 197.4694 165.19144
## 45.71429 142.4458 108.98048
## 45.85714 247.1424 212.60710
## 46.00000 228.0545 189.85075
## 46.14286 307.7549 267.51290
## 46.28571 276.9731 235.25885
## 46.42857 286.5420 243.50079
## 46.57143 183.6623 139.42009
## 46.71429 128.5173  83.18427
## 46.85714 233.0649 186.73824
## 
## $東京都$upper
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##               80%      95%
## 45.00000 335.5507 359.5885
## 45.14286 426.4839 454.2665
## 45.28571 402.0017 431.4680
## 45.42857 417.2124 448.1658
## 45.57143 319.4182 351.6961
## 45.71429 268.8807 302.3460
## 45.85714 377.6200 412.1553
## 46.00000 372.3917 410.5955
## 46.14286 459.7927 500.0347
## 46.28571 434.5732 476.2874
## 46.42857 449.1554 492.1966
## 46.57143 350.8134 395.0556
## 46.71429 299.7896 345.1227
## 46.85714 408.0913 454.4180
## 
## 
## $神奈川県
## $神奈川県$mean
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##  [1] 138.4486 165.6593 155.2635 158.2929 127.9101 104.3612 148.3092 159.5245
##  [9] 171.4298 165.3023 165.2421 147.4851 127.9090 161.4508
## 
## $神奈川県$lower
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##                80%       95%
## 45.00000 119.37363 109.27596
## 45.14286 144.45208 133.22567
## 45.28571 132.71953 120.78549
## 45.42857 135.55338 123.51582
## 45.57143 104.83495  92.61972
## 45.71429  80.69445  68.16603
## 45.85714 122.97850 109.56923
## 46.00000 129.82743 114.10674
## 46.14286 139.80443 123.06297
## 46.28571 132.53710 115.19228
## 46.42857 131.78814 114.07866
## 46.57143 113.17209  95.00785
## 46.71429  92.39663  73.59750
## 46.85714 124.36892 104.73896
## 
## $神奈川県$upper
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##               80%      95%
## 45.00000 157.5235 167.6212
## 45.14286 186.8665 198.0929
## 45.28571 177.8074 189.7415
## 45.42857 181.0324 193.0699
## 45.57143 150.9852 163.2004
## 45.71429 128.0279 140.5563
## 45.85714 173.6399 187.0492
## 46.00000 189.2216 204.9423
## 46.14286 203.0552 219.7966
## 46.28571 198.0674 215.4122
## 46.42857 198.6961 216.4056
## 46.57143 181.7982 199.9624
## 46.71429 163.4214 182.2206
## 46.85714 198.5327 218.1627
## 
## 
## $新潟県
## $新潟県$mean
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##  [1] 8.232616 8.232616 8.232616 8.232616 8.232616 8.232616 8.232616 8.232616
##  [9] 8.232616 8.232616 8.232616 8.232616 8.232616 8.232616
## 
## $新潟県$lower
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##               80%      95%
## 45.00000 5.347492 3.820199
## 45.14286 5.299659 3.747045
## 45.28571 5.252594 3.675066
## 45.42857 5.206261 3.604205
## 45.57143 5.160627 3.534413
## 45.71429 5.115660 3.465643
## 45.85714 5.071333 3.397851
## 46.00000 5.027620 3.330997
## 46.14286 4.984494 3.265042
## 46.28571 4.941934 3.199951
## 46.42857 4.899917 3.135692
## 46.57143 4.858423 3.072233
## 46.71429 4.817433 3.009544
## 46.85714 4.776930 2.947600
## 
## $新潟県$upper
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##               80%      95%
## 45.00000 11.11774 12.64503
## 45.14286 11.16557 12.71819
## 45.28571 11.21264 12.79017
## 45.42857 11.25897 12.86103
## 45.57143 11.30461 12.93082
## 45.71429 11.34957 12.99959
## 45.85714 11.39390 13.06738
## 46.00000 11.43761 13.13424
## 46.14286 11.48074 13.20019
## 46.28571 11.52330 13.26528
## 46.42857 11.56532 13.32954
## 46.57143 11.60681 13.39300
## 46.71429 11.64780 13.45569
## 46.85714 11.68830 13.51763
## 
## 
## $富山県
## $富山県$mean
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##  [1] 1.213055 1.112680 1.133025 1.138783 1.170872 1.168133 1.179459 1.187185
##  [9] 1.197798 1.204102 1.211700 1.218353 1.225021 1.230866
## 
## $富山県$lower
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##                80%       95%
## 45.00000 -1.233213 -2.528188
## 45.14286 -1.465516 -2.830330
## 45.28571 -1.593858 -3.037382
## 45.42857 -1.697497 -3.198932
## 45.57143 -1.835077 -3.426331
## 45.71429 -1.934615 -3.577111
## 45.85714 -2.012237 -3.701819
## 46.00000 -2.078299 -3.806942
## 46.14286 -2.137082 -3.902461
## 46.28571 -2.187280 -3.982569
## 46.42857 -2.229403 -4.051012
## 46.57143 -2.265635 -4.109948
## 46.71429 -2.296779 -4.161108
## 46.85714 -2.323615 -4.205243
## 
## $富山県$upper
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##               80%      95%
## 45.00000 3.659322 4.954298
## 45.14286 3.690876 5.055690
## 45.28571 3.859908 5.303433
## 45.42857 3.975062 5.476498
## 45.57143 4.176821 5.768074
## 45.71429 4.270881 5.913377
## 45.85714 4.371155 6.060737
## 46.00000 4.452669 6.181312
## 46.14286 4.532678 6.298056
## 46.28571 4.595485 6.390774
## 46.42857 4.652802 6.474412
## 46.57143 4.702342 6.546654
## 46.71429 4.746822 6.611151
## 46.85714 4.785347 6.666975
## 
## 
## $石川県
## $石川県$mean
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##  [1] 0.5906268 0.5906268 0.5906268 0.5906268 0.5906268 0.5906268 0.5906268
##  [8] 0.5906268 0.5906268 0.5906268 0.5906268 0.5906268 0.5906268 0.5906268
## 
## $石川県$lower
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##                80%       95%
## 45.00000 -3.076025 -5.017034
## 45.14286 -3.282028 -5.332088
## 45.28571 -3.477614 -5.631210
## 45.42857 -3.664217 -5.916596
## 45.57143 -3.842974 -6.189981
## 45.71429 -4.014798 -6.452763
## 45.85714 -4.180438 -6.706087
## 46.00000 -4.340517 -6.950907
## 46.14286 -4.495560 -7.188025
## 46.28571 -4.646015 -7.418126
## 46.42857 -4.792267 -7.641798
## 46.57143 -4.934648 -7.859552
## 46.71429 -5.073451 -8.071833
## 46.85714 -5.208934 -8.279036
## 
## $石川県$upper
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##               80%      95%
## 45.00000 4.257279 6.198287
## 45.14286 4.463282 6.513342
## 45.28571 4.658867 6.812463
## 45.42857 4.845471 7.097849
## 45.57143 5.024228 7.371235
## 45.71429 5.196052 7.634017
## 45.85714 5.361692 7.887341
## 46.00000 5.521771 8.132161
## 46.14286 5.676814 8.369279
## 46.28571 5.827269 8.599380
## 46.42857 5.973520 8.823052
## 46.57143 6.115902 9.040805
## 46.71429 6.254705 9.253087
## 46.85714 6.390187 9.460289
## 
## 
## $福井県
## $福井県$mean
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##  [1] 2.149185 3.037748 2.665876 1.943133 2.390488 1.967890 1.737610 1.890659
##  [9] 1.593564 1.538447 1.550082 1.377358 1.367624 1.331188
## 
## $福井県$lower
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##                 80%         95%
## 45.00000  0.3082890 -0.66622232
## 45.14286  1.0851999  0.05158364
## 45.28571  0.5172927 -0.62009879
## 45.42857 -0.4505707 -1.71772080
## 45.57143 -0.0489557 -1.34031935
## 45.71429 -0.5691326 -1.91215128
## 45.85714 -0.8721243 -2.25363405
## 46.00000 -0.7446877 -2.13975568
## 46.14286 -1.0867557 -2.50563140
## 46.28571 -1.1673151 -2.59965908
## 46.42857 -1.1703649 -2.61048253
## 46.57143 -1.3625249 -2.81293113
## 46.71429 -1.3824367 -2.83823095
## 46.85714 -1.4268244 -2.88682780
## 
## $福井県$upper
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##               80%      95%
## 45.00000 3.990080 4.964592
## 45.14286 4.990295 6.023912
## 45.28571 4.814460 5.951852
## 45.42857 4.336836 5.603987
## 45.57143 4.829932 6.121296
## 45.71429 4.504913 5.847932
## 45.85714 4.347344 5.728853
## 46.00000 4.526005 5.921073
## 46.14286 4.273885 5.692760
## 46.28571 4.244209 5.676553
## 46.42857 4.270529 5.710647
## 46.57143 4.117240 5.567647
## 46.71429 4.117685 5.573479
## 46.85714 4.089200 5.549203
## 
## 
## $山梨県
## $山梨県$mean
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##  [1] 2.882889 3.510368 3.719477 3.789164 3.812387 3.820126 3.822705 3.823565
##  [9] 3.823851 3.823947 3.823978 3.823989 3.823993 3.823994
## 
## $山梨県$lower
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##               80%        95%
## 45.00000 1.079602 0.12499945
## 45.14286 1.510167 0.45132473
## 45.28571 1.644272 0.54572440
## 45.42857 1.665999 0.54206336
## 45.57143 1.649233 0.50412889
## 45.71429 1.619855 0.45510133
## 45.85714 1.586622 0.40291137
## 46.00000 1.552460 0.35020946
## 46.14286 1.518330 0.29786075
## 46.28571 1.484540 0.24613232
## 46.42857 1.451177 0.19509242
## 46.57143 1.418260 0.14474417
## 46.71429 1.385781 0.09507019
## 46.85714 1.353727 0.04604746
## 
## $山梨県$upper
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##               80%      95%
## 45.00000 4.686176 5.640778
## 45.14286 5.510569 6.569411
## 45.28571 5.794683 6.893230
## 45.42857 5.912328 7.036264
## 45.57143 5.975541 7.120645
## 45.71429 6.020398 7.185151
## 45.85714 6.058788 7.242499
## 46.00000 6.094670 7.296920
## 46.14286 6.129372 7.349842
## 46.28571 6.163354 7.401761
## 46.42857 6.196780 7.452865
## 46.57143 6.229718 7.503234
## 46.71429 6.262204 7.552915
## 46.85714 6.294260 7.601940
## 
## 
## $長野県
## $長野県$mean
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##  [1] 18.86588 20.34278 19.63832 19.83824 19.83163 19.93455 19.97994 19.99953
##  [9] 19.98582 19.96125 19.93932 19.92859 19.92893 19.93581
## 
## $長野県$lower
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##               80%      95%
## 45.00000 15.86705 14.27956
## 45.14286 17.25118 15.61458
## 45.28571 16.40573 14.69450
## 45.42857 16.48043 14.70291
## 45.57143 16.22761 14.31976
## 45.71429 15.98744 13.89796
## 45.85714 15.64551 13.35101
## 46.00000 15.30563 12.82083
## 46.14286 14.98897 12.34380
## 46.28571 14.71439 11.93686
## 46.42857 14.47652 11.58468
## 46.57143 14.26510 11.26702
## 46.71429 14.06736 10.96443
## 46.85714 13.87371 10.66462
## 
## $長野県$upper
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##               80%      95%
## 45.00000 21.86472 23.45220
## 45.14286 23.43438 25.07098
## 45.28571 22.87090 24.58213
## 45.42857 23.19606 24.97358
## 45.57143 23.43564 25.34349
## 45.71429 23.88166 25.97114
## 45.85714 24.31436 26.60886
## 46.00000 24.69344 27.17824
## 46.14286 24.98267 27.62785
## 46.28571 25.20811 27.98563
## 46.42857 25.40213 28.29396
## 46.57143 25.59208 28.59015
## 46.71429 25.79050 28.89343
## 46.85714 25.99791 29.20700
## 
## 
## $岐阜県
## $岐阜県$mean
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##  [1] 11.833955 14.630370 12.586803  9.960603 11.611986 12.758318 12.859699
##  [8] 12.534988 13.480119 12.176455 11.704838 12.234736 12.775693 12.464388
## 
## $岐阜県$lower
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##                80%      95%
## 45.00000  8.427179 6.623740
## 45.14286 10.734884 8.672738
## 45.28571  8.432816 6.233828
## 45.42857  5.604626 3.298711
## 45.57143  7.073974 4.671695
## 45.71429  8.048355 5.555052
## 45.85714  7.984712 5.404050
## 46.00000  7.203932 4.381841
## 46.14286  7.867337 4.896110
## 46.28571  6.329622 3.234496
## 46.42857  5.642256 2.432919
## 46.57143  5.966431 2.648190
## 46.71429  6.308946 2.885657
## 46.85714  5.805321 2.280224
## 
## $岐阜県$upper
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##               80%      95%
## 45.00000 15.24073 17.04417
## 45.14286 18.52586 20.58800
## 45.28571 16.74079 18.93978
## 45.42857 14.31658 16.62249
## 45.57143 16.15000 18.55228
## 45.71429 17.46828 19.96158
## 45.85714 17.73469 20.31535
## 46.00000 17.86604 20.68813
## 46.14286 19.09290 22.06413
## 46.28571 18.02329 21.11841
## 46.42857 17.76742 20.97676
## 46.57143 18.50304 21.82128
## 46.71429 19.24244 22.66573
## 46.85714 19.12345 22.64855
## 
## 
## $静岡県
## $静岡県$mean
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##  [1] 22.69680 17.74024 16.08554 24.24645 21.60459 15.44166 18.72548 22.78338
##  [9] 18.15896 17.32057 23.03501 21.22576 17.15957 19.34771
## 
## $静岡県$lower
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##                80%       95%
## 45.00000 18.662719 16.527205
## 45.14286 13.092033 10.631421
## 45.28571 11.241263  8.676859
## 45.42857 19.252665 16.609115
## 45.57143 16.184500 13.315277
## 45.71429  9.723813  6.696967
## 45.85714 12.888061  9.797920
## 46.00000 16.599737 13.326315
## 46.14286 11.591871  8.115464
## 46.28571 10.581009  7.013302
## 46.42857 16.124372 12.466100
## 46.57143 13.994677 10.166773
## 46.71429  9.698913  5.749481
## 46.85714 11.747516  7.724215
## 
## $静岡県$upper
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##               80%      95%
## 45.00000 26.73088 28.86640
## 45.14286 22.38844 24.84906
## 45.28571 20.92981 23.49422
## 45.42857 29.24024 31.88379
## 45.57143 27.02468 29.89390
## 45.71429 21.15951 24.18635
## 45.85714 24.56289 27.65303
## 46.00000 28.96702 32.24044
## 46.14286 24.72605 28.20246
## 46.28571 24.06012 27.62783
## 46.42857 29.94566 33.60393
## 46.57143 28.45684 32.28475
## 46.71429 24.62022 28.56965
## 46.85714 26.94791 30.97121
## 
## 
## $愛知県
## $愛知県$mean
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##  [1] 115.31911 140.12045 145.95884 146.10087 122.05098  97.27238 142.54708
##  [8] 131.74574 144.91646 151.10045 152.60497 131.55295 117.29933 148.01240
## 
## $愛知県$lower
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##                80%       95%
## 45.00000 100.22648  92.23693
## 45.14286 121.35116 111.41530
## 45.28571 122.91817 110.72118
## 45.42857 119.33538 105.16659
## 45.57143  92.95464  77.55197
## 45.71429  64.87330  47.72226
## 45.85714 107.93423  89.61129
## 46.00000  91.68730  70.48165
## 46.14286 100.45586  76.91983
## 46.28571 102.94865  77.45862
## 46.42857 100.33654  72.66731
## 46.57143  76.12201  46.77865
## 46.71429  58.53781  27.43133
## 46.85714  86.05813  53.26152
## 
## $愛知県$upper
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##               80%      95%
## 45.00000 130.4117 138.4013
## 45.14286 158.8897 168.8256
## 45.28571 168.9995 181.1965
## 45.42857 172.8664 187.0352
## 45.57143 151.1473 166.5500
## 45.71429 129.6715 146.8225
## 45.85714 177.1599 195.4829
## 46.00000 171.8042 193.0098
## 46.14286 189.3771 212.9131
## 46.28571 199.2523 224.7423
## 46.42857 204.8734 232.5426
## 46.57143 186.9839 216.3273
## 46.71429 176.0609 207.1673
## 46.85714 209.9667 242.7633
## 
## 
## $三重県
## $三重県$mean
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##  [1] 4.423929 4.423929 4.423929 4.423929 4.423929 4.423929 4.423929 4.423929
##  [9] 4.423929 4.423929 4.423929 4.423929 4.423929 4.423929
## 
## $三重県$lower
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##                 80%       95%
## 45.00000  0.7266055 -1.230639
## 45.14286  0.5260549 -1.537355
## 45.28571  0.3353298 -1.829044
## 45.42857  0.1531135 -2.107720
## 45.57143 -0.0216403 -2.374983
## 45.71429 -0.1897797 -2.632130
## 45.85714 -0.3520033 -2.880229
## 46.00000 -0.5088948 -3.120174
## 46.14286 -0.6609478 -3.352719
## 46.28571 -0.8085841 -3.578509
## 46.42857 -0.9521676 -3.798101
## 46.57143 -1.0920148 -4.011979
## 46.71429 -1.2284030 -4.220567
## 46.85714 -1.3615769 -4.424239
## 
## $三重県$upper
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##                80%      95%
## 45.00000  8.121253 10.07850
## 45.14286  8.321804 10.38521
## 45.28571  8.512529 10.67690
## 45.42857  8.694745 10.95558
## 45.57143  8.869499 11.22284
## 45.71429  9.037638 11.47999
## 45.85714  9.199862 11.72809
## 46.00000  9.356753 11.96803
## 46.14286  9.508806 12.20058
## 46.28571  9.656443 12.42637
## 46.42857  9.800026 12.64596
## 46.57143  9.939873 12.85984
## 46.71429 10.076262 13.06843
## 46.85714 10.209435 13.27210
## 
## 
## $滋賀県
## $滋賀県$mean
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##  [1] 8.720768 9.300269 8.588465 8.755067 8.532405 8.579842 8.510071 8.523416
##  [9] 8.501515 8.505211 8.498324 8.499327 8.497157 8.497421
## 
## $滋賀県$lower
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##               80%       95%
## 45.00000 4.834464 2.7771795
## 45.14286 5.352015 3.2619361
## 45.28571 4.226457 1.9173497
## 45.42857 4.307730 1.9534523
## 45.57143 3.923172 1.4831910
## 45.71429 3.882620 1.3960612
## 45.85714 3.706175 1.1631465
## 46.00000 3.633755 1.0453243
## 46.14286 3.521972 0.8859608
## 46.28571 3.442780 0.7628902
## 46.42857 3.352767 0.6288733
## 46.57143 3.273576 0.5072300
## 46.71429 3.191980 0.3835885
## 46.85714 3.114475 0.2649154
## 
## $滋賀県$upper
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##               80%      95%
## 45.00000 12.60707 14.66436
## 45.14286 13.24852 15.33860
## 45.28571 12.95047 15.25958
## 45.42857 13.20240 15.55668
## 45.57143 13.14164 15.58162
## 45.71429 13.27706 15.76362
## 45.85714 13.31397 15.85700
## 46.00000 13.41308 16.00151
## 46.14286 13.48106 16.11707
## 46.28571 13.56764 16.24753
## 46.42857 13.64388 16.36777
## 46.57143 13.72508 16.49142
## 46.71429 13.80233 16.61072
## 46.85714 13.88037 16.72993
## 
## 
## $京都府
## $京都府$mean
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##  [1] 30.27119 25.18940 29.08234 27.45267 27.49008 23.89872 31.24559 28.81546
##  [9] 27.67146 29.12262 28.31491 28.02548 26.68797 33.30787
## 
## $京都府$lower
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##               80%      95%
## 45.00000 23.49552 19.90871
## 45.14286 17.89151 14.02824
## 45.28571 21.61760 17.66601
## 45.42857 19.82474 15.78675
## 45.57143 19.70237 15.57980
## 45.71429 15.95444 11.74899
## 45.85714 23.14777 18.86105
## 46.00000 20.24284 15.70477
## 46.14286 18.85314 14.18500
## 46.28571 20.11078 15.34019
## 46.42857 19.11362 14.24275
## 46.57143 18.63855 13.66941
## 46.71429 17.11901 12.05351
## 46.85714 23.56029 18.40023
## 
## $京都府$upper
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##               80%      95%
## 45.00000 37.04685 40.63367
## 45.14286 32.48729 36.35056
## 45.28571 36.54708 40.49867
## 45.42857 35.08061 39.11859
## 45.57143 35.27780 39.40036
## 45.71429 31.84300 36.04845
## 45.85714 39.34341 43.63014
## 46.00000 37.38807 41.92614
## 46.14286 36.48978 41.15792
## 46.28571 38.13447 42.90505
## 46.42857 37.51621 42.38708
## 46.57143 37.41240 42.38154
## 46.71429 36.25692 41.32242
## 46.85714 43.05546 48.21552
## 
## 
## $大阪府
## $大阪府$mean
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##  [1] 243.4380 246.9082 277.3963 295.3299 268.1006 158.8400 300.9998 300.5490
##  [9] 292.9317 318.3392 334.4499 315.2731 195.4832 331.1209
## 
## $大阪府$lower
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##               80%      95%
## 45.00000 217.1525 203.2378
## 45.14286 218.2904 203.1410
## 45.28571 246.6223 230.3316
## 45.42857 262.5412 245.1840
## 45.57143 233.4142 215.0523
## 45.71429 122.3543 103.0400
## 45.85714 262.7995 242.5775
## 46.00000 255.9081 232.2767
## 46.14286 245.3776 220.2040
## 46.28571 268.0402 241.4136
## 46.42857 281.5484 253.5440
## 46.57143 259.8912 230.5738
## 46.71429 137.7273 107.1531
## 46.85714 271.0848 239.3036
## 
## $大阪府$upper
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##               80%      95%
## 45.00000 269.7235 283.6382
## 45.14286 275.5261 290.6755
## 45.28571 308.1703 324.4611
## 45.42857 328.1185 345.4757
## 45.57143 302.7871 321.1490
## 45.71429 195.3257 214.6401
## 45.85714 339.2001 359.4221
## 46.00000 345.1899 368.8213
## 46.14286 340.4859 365.6595
## 46.28571 368.6381 395.2648
## 46.42857 387.3515 415.3558
## 46.57143 370.6551 399.9725
## 46.71429 253.2391 283.8133
## 46.85714 391.1570 422.9382
## 
## 
## $兵庫県
## $兵庫県$mean
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##  [1]  93.39722  99.19108  94.08874  97.37207  97.02982  76.75623 114.05909
##  [8] 108.44792 112.48678 108.92998 111.21876 110.98019  96.84759 122.85119
## 
## $兵庫県$lower
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##                80%      95%
## 45.00000  83.63936 78.47386
## 45.14286  88.79874 83.29736
## 45.28571  83.09849 77.28061
## 45.42857  85.81480 79.69675
## 45.57143  84.93208 78.52793
## 45.71429  64.14116 57.46314
## 45.85714 100.94708 94.00599
## 46.00000  92.93061 84.71624
## 46.14286  96.08733 87.40598
## 46.28571  91.69347 82.56901
## 46.42857  93.18401 83.63698
## 46.57143  92.18105 82.22938
## 46.71429  77.31396 66.97348
## 46.85714 102.60970 91.89450
## 
## $兵庫県$upper
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##                80%       95%
## 45.00000 103.15508 108.32058
## 45.14286 109.58342 115.08479
## 45.28571 105.07899 110.89688
## 45.42857 108.92934 115.04738
## 45.57143 109.12756 115.53172
## 45.71429  89.37131  96.04933
## 45.85714 127.17111 134.11220
## 46.00000 123.96523 132.17960
## 46.14286 128.88624 137.56758
## 46.28571 126.16649 135.29094
## 46.42857 129.25352 138.80054
## 46.57143 129.77932 139.73099
## 46.71429 116.38122 126.72170
## 46.85714 143.09268 153.80788
## 
## 
## $奈良県
## $奈良県$mean
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##  [1] 13.69095 15.44196 15.49836 14.93702 13.47934 13.29385 13.33635 13.81544
##  [9] 14.12023 14.13224 14.02450 13.85479 13.80378 13.82416
## 
## $奈良県$lower
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##                80%      95%
## 45.00000  9.869806 7.847016
## 45.14286 11.427511 9.302392
## 45.28571 10.972872 8.577222
## 45.42857 10.353221 7.926703
## 45.57143  8.824622 6.360563
## 45.71429  8.513510 5.982951
## 45.85714  8.414213 5.808591
## 46.00000  8.673150 5.950985
## 46.14286  8.806054 5.992899
## 46.28571  8.659216 5.761974
## 46.42857  8.420836 5.454433
## 46.57143  8.127617 5.095833
## 46.71429  7.947696 4.847675
## 46.85714  7.836866 4.667385
## 
## $奈良県$upper
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##               80%      95%
## 45.00000 17.51209 19.53488
## 45.14286 19.45640 21.58152
## 45.28571 20.02386 22.41951
## 45.42857 19.52082 21.94734
## 45.57143 18.13406 20.59812
## 45.71429 18.07419 20.60475
## 45.85714 18.25848 20.86410
## 46.00000 18.95773 21.67990
## 46.14286 19.43441 22.24756
## 46.28571 19.60526 22.50250
## 46.42857 19.62817 22.59458
## 46.57143 19.58197 22.61375
## 46.71429 19.65985 22.75988
## 46.85714 19.81145 22.98093
## 
## 
## $和歌山県
## $和歌山県$mean
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##  [1] 5.293123 5.720182 5.797476 5.008050 5.200434 5.528338 5.665248 5.632417
##  [9] 5.483413 5.611564 5.501347 5.596140 5.514613 5.584731
## 
## $和歌山県$lower
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##               80%      95%
## 45.00000 3.355514 2.329805
## 45.14286 3.607552 2.489193
## 45.28571 3.615649 2.460659
## 45.42857 2.684039 1.453782
## 45.57143 2.804934 1.536834
## 45.71429 3.012224 1.680274
## 45.85714 3.076887 1.706691
## 46.00000 3.003141 1.611286
## 46.14286 2.806452 1.389354
## 46.28571 2.856823 1.398550
## 46.42857 2.697288 1.212909
## 46.57143 2.721676 1.200026
## 46.71429 2.590087 1.041937
## 46.85714 2.595371 1.012899
## 
## $和歌山県$upper
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##               80%       95%
## 45.00000 7.230733  8.256441
## 45.14286 7.832813  8.951172
## 45.28571 7.979304  9.134294
## 45.42857 7.332061  8.562318
## 45.57143 7.595933  8.864034
## 45.71429 8.044452  9.376402
## 45.85714 8.253610  9.623806
## 46.00000 8.261693  9.653548
## 46.14286 8.160375  9.577472
## 46.28571 8.366306  9.824579
## 46.42857 8.305406  9.789785
## 46.57143 8.470604  9.992254
## 46.71429 8.439139  9.987289
## 46.85714 8.574091 10.156562
## 
## 
## $鳥取県
## $鳥取県$mean
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##  [1] 0.1890774 0.2140578 0.2407070 0.2760103 0.2877568 0.2977130 0.3058372
##  [8] 0.3099229 0.3129771 0.3151182 0.3163983 0.3172954 0.3178970 0.3182816
## 
## $鳥取県$lower
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##                 80%       95%
## 45.00000 -0.7477869 -1.243733
## 45.14286 -0.7326129 -1.233750
## 45.28571 -0.7171222 -1.224166
## 45.42857 -0.7005537 -1.217515
## 45.57143 -0.6917531 -1.210274
## 45.71429 -0.6841400 -1.203902
## 45.85714 -0.6778082 -1.198519
## 46.00000 -0.6745298 -1.195668
## 46.14286 -0.6720981 -1.193565
## 46.28571 -0.6704347 -1.192155
## 46.42857 -0.6695122 -1.191422
## 46.57143 -0.6689254 -1.190999
## 46.71429 -0.6685997 -1.190819
## 46.85714 -0.6684669 -1.190820
## 
## $鳥取県$upper
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##               80%      95%
## 45.00000 1.125942 1.621888
## 45.14286 1.160729 1.661866
## 45.28571 1.198536 1.705580
## 45.42857 1.252574 1.769536
## 45.57143 1.267267 1.785788
## 45.71429 1.279566 1.799328
## 45.85714 1.289483 1.810193
## 46.00000 1.294376 1.815514
## 46.14286 1.298052 1.819520
## 46.28571 1.300671 1.822391
## 46.42857 1.302309 1.824218
## 46.57143 1.303516 1.825590
## 46.71429 1.304394 1.826613
## 46.85714 1.305030 1.827383
## 
## 
## $島根県
## $島根県$mean
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##  [1] 0.461039 0.461039 0.461039 0.461039 0.461039 0.461039 0.461039 0.461039
##  [9] 0.461039 0.461039 0.461039 0.461039 0.461039 0.461039
## 
## $島根県$lower
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##                80%       95%
## 45.00000 -6.288083 -9.860853
## 45.14286 -6.288083 -9.860853
## 45.28571 -6.288083 -9.860853
## 45.42857 -6.288083 -9.860853
## 45.57143 -6.288083 -9.860853
## 45.71429 -6.288083 -9.860853
## 45.85714 -6.288083 -9.860853
## 46.00000 -6.288083 -9.860853
## 46.14286 -6.288083 -9.860853
## 46.28571 -6.288083 -9.860853
## 46.42857 -6.288083 -9.860853
## 46.57143 -6.288083 -9.860853
## 46.71429 -6.288083 -9.860853
## 46.85714 -6.288083 -9.860853
## 
## $島根県$upper
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##               80%      95%
## 45.00000 7.210161 10.78293
## 45.14286 7.210161 10.78293
## 45.28571 7.210161 10.78293
## 45.42857 7.210161 10.78293
## 45.57143 7.210161 10.78293
## 45.71429 7.210161 10.78293
## 45.85714 7.210161 10.78293
## 46.00000 7.210161 10.78293
## 46.14286 7.210161 10.78293
## 46.28571 7.210161 10.78293
## 46.42857 7.210161 10.78293
## 46.57143 7.210161 10.78293
## 46.71429 7.210161 10.78293
## 46.85714 7.210161 10.78293
## 
## 
## $岡山県
## $岡山県$mean
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##  [1]  7.348138  7.217914 10.644506  8.733906  9.980493  8.312108  8.097425
##  [8]  8.012940  8.879237  8.377425  8.319179  8.882467  8.464509  8.499288
## 
## $岡山県$lower
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##               80%      95%
## 45.00000 4.757015 3.385358
## 45.14286 4.367365 2.858376
## 45.28571 7.513270 5.855694
## 45.42857 5.578256 3.907756
## 45.57143 6.811126 5.133364
## 45.71429 5.048215 3.320414
## 45.85714 4.823045 3.089693
## 46.00000 4.609613 2.808000
## 46.14286 5.349167 3.480461
## 46.28571 4.800845 2.907517
## 46.42857 4.710865 2.800739
## 46.57143 5.214491 3.272781
## 46.71429 4.764949 2.806521
## 46.85714 4.768617 2.793719
## 
## $岡山県$upper
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##               80%      95%
## 45.00000  9.93926 11.31092
## 45.14286 10.06846 11.57745
## 45.28571 13.77574 15.43332
## 45.42857 11.88956 13.56006
## 45.57143 13.14986 14.82762
## 45.71429 11.57600 13.30380
## 45.85714 11.37180 13.10516
## 46.00000 11.41627 13.21788
## 46.14286 12.40931 14.27801
## 46.28571 11.95401 13.84733
## 46.42857 11.92749 13.83762
## 46.57143 12.55044 14.49215
## 46.71429 12.16407 14.12250
## 46.85714 12.22996 14.20486
## 
## 
## $広島県
## $広島県$mean
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##  [1] 4.852275 5.141823 3.578633 3.769128 4.549274 4.278063 3.927332 4.146833
##  [9] 4.283118 4.137825 4.098023 4.183144 4.185408 4.140156
## 
## $広島県$lower
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##                 80%        95%
## 45.00000  1.3274461 -0.5384858
## 45.14286  1.2211702 -0.8542978
## 45.28571 -0.5315439 -2.7073403
## 45.42857 -0.9360876 -3.4268784
## 45.57143 -0.6935468 -3.4689287
## 45.71429 -1.2497981 -4.1760709
## 45.85714 -1.9061230 -4.9941678
## 46.00000 -2.0645713 -5.3526900
## 46.14286 -2.2472297 -5.7041867
## 46.28571 -2.6631972 -6.2634408
## 46.42857 -2.9873156 -6.7380676
## 46.57143 -3.1869858 -7.0884968
## 46.71429 -3.4444546 -7.4834599
## 46.85714 -3.7383890 -7.9090388
## 
## $広島県$upper
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##                80%       95%
## 45.00000  8.377104 10.243036
## 45.14286  9.062475 11.137943
## 45.28571  7.688811  9.864607
## 45.42857  8.474344 10.965135
## 45.57143  9.792096 12.567477
## 45.71429  9.805924 12.732197
## 45.85714  9.760788 12.848833
## 46.00000 10.358237 13.646355
## 46.14286 10.813465 14.270422
## 46.28571 10.938847 14.539091
## 46.42857 11.183363 14.934114
## 46.57143 11.553274 15.454785
## 46.71429 11.815271 15.854276
## 46.85714 12.018701 16.189351
## 
## 
## $山口県
## $山口県$mean
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##  [1] 7.298146 7.298146 7.298146 7.298146 7.298146 7.298146 7.298146 7.298146
##  [9] 7.298146 7.298146 7.298146 7.298146 7.298146 7.298146
## 
## $山口県$lower
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##               80%      95%
## 45.00000 5.069604 3.889885
## 45.14286 4.975643 3.746184
## 45.28571 4.885338 3.608074
## 45.42857 4.798293 3.474951
## 45.57143 4.714179 3.346309
## 45.71429 4.632717 3.221725
## 45.85714 4.553673 3.100837
## 46.00000 4.476843 2.983335
## 46.14286 4.402049 2.868949
## 46.28571 4.329140 2.757443
## 46.42857 4.257978 2.648611
## 46.57143 4.188445 2.542269
## 46.71429 4.120432 2.438253
## 46.85714 4.053846 2.336417
## 
## $山口県$upper
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##                80%      95%
## 45.00000  9.526688 10.70641
## 45.14286  9.620649 10.85011
## 45.28571  9.710954 10.98822
## 45.42857  9.797999 11.12134
## 45.57143  9.882113 11.24998
## 45.71429  9.963574 11.37457
## 45.85714 10.042619 11.49545
## 46.00000 10.119449 11.61296
## 46.14286 10.194242 11.72734
## 46.28571 10.267152 11.83885
## 46.42857 10.338313 11.94768
## 46.57143 10.407847 12.05402
## 46.71429 10.475859 12.15804
## 46.85714 10.542446 12.25988
## 
## 
## $徳島県
## $徳島県$mean
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##  [1] 0.753228836 0.513692824 0.005329393 0.906012591 0.556645569 0.386842605
##  [7] 0.639187104 0.718243550 0.672276357 0.748522493 0.470791291 0.661820495
## [13] 0.686101562 0.418641525
## 
## $徳島県$lower
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##                 80%       95%
## 45.00000 -0.9733703 -1.887377
## 45.14286 -1.2949753 -2.252426
## 45.28571 -1.8218279 -2.789067
## 45.42857 -0.9394486 -1.916377
## 45.57143 -1.3069397 -2.293462
## 45.71429 -1.4946922 -2.490717
## 45.85714 -1.2601276 -2.265564
## 46.00000 -1.2751834 -2.330440
## 46.14286 -1.3546912 -2.427703
## 46.28571 -1.3021999 -2.387787
## 46.42857 -1.6034139 -2.701432
## 46.57143 -1.4356046 -2.545914
## 46.71429 -1.4342891 -2.556756
## 46.85714 -1.7244687 -2.858963
## 
## $徳島県$upper
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##               80%      95%
## 45.00000 2.479828 3.393834
## 45.14286 2.322361 3.279812
## 45.28571 1.832487 2.799725
## 45.42857 2.751474 3.728402
## 45.57143 2.420231 3.406753
## 45.71429 2.268377 3.264402
## 45.85714 2.538502 3.543938
## 46.00000 2.711671 3.766927
## 46.14286 2.699244 3.772256
## 46.28571 2.799245 3.884832
## 46.42857 2.544996 3.643014
## 46.57143 2.759246 3.869555
## 46.71429 2.806492 3.928959
## 46.85714 2.561752 3.696246
## 
## 
## $香川県
## $香川県$mean
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##  [1] 0.8534792 1.1752393 1.1752393 1.1752393 1.1752393 1.1752393 1.1752393
##  [8] 1.1752393 1.1752393 1.1752393 1.1752393 1.1752393 1.1752393 1.1752393
## 
## $香川県$lower
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##                 80%       95%
## 45.00000 -0.5856459 -1.347473
## 45.14286 -0.3259395 -1.120616
## 45.28571 -0.3318828 -1.129705
## 45.42857 -0.3378028 -1.138759
## 45.57143 -0.3436998 -1.147778
## 45.71429 -0.3495739 -1.156761
## 45.85714 -0.3554255 -1.165710
## 46.00000 -0.3612548 -1.174626
## 46.14286 -0.3670621 -1.183507
## 46.28571 -0.3728475 -1.192355
## 46.42857 -0.3786115 -1.201170
## 46.57143 -0.3843541 -1.209953
## 46.71429 -0.3900757 -1.218703
## 46.85714 -0.3957764 -1.227422
## 
## $香川県$upper
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##               80%      95%
## 45.00000 2.292604 3.054431
## 45.14286 2.676418 3.471094
## 45.28571 2.682361 3.480184
## 45.42857 2.688281 3.489237
## 45.57143 2.694178 3.498256
## 45.71429 2.700052 3.507240
## 45.85714 2.705904 3.516189
## 46.00000 2.711733 3.525104
## 46.14286 2.717541 3.533986
## 46.28571 2.723326 3.542834
## 46.42857 2.729090 3.551649
## 46.57143 2.734833 3.560431
## 46.71429 2.740554 3.569182
## 46.85714 2.746255 3.577900
## 
## 
## $愛媛県
## $愛媛県$mean
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##  [1] 2.0348391 2.9430443 1.0531906 2.4076243 0.9828140 1.9907281 0.9161410
##  [8] 1.6657728 0.8550159 1.4122153 0.8002693 1.2141629 0.7520818 1.0593072
## 
## $愛媛県$lower
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##                 80%        95%
## 45.00000  0.2804646 -0.6482452
## 45.14286  1.1542881  0.2073777
## 45.28571 -0.7786085 -1.7483044
## 45.42857  0.5738158 -0.3969437
## 45.57143 -0.8767404 -1.8611290
## 45.71429  0.1296425 -0.8555567
## 45.85714 -0.9604994 -1.9539329
## 46.00000 -0.2120105 -1.2060490
## 46.14286 -1.0322280 -2.0312746
## 46.28571 -0.4758668 -1.4753570
## 46.42857 -1.0935941 -2.0961449
## 46.57143 -0.6803060 -1.6831773
## 46.71429 -1.1459330 -2.1506813
## 46.85714 -0.8391393 -1.8441162
## 
## $愛媛県$upper
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##               80%      95%
## 45.00000 3.789214 4.717924
## 45.14286 4.731801 5.678711
## 45.28571 2.884990 3.854686
## 45.42857 4.241433 5.212192
## 45.57143 2.842368 3.826757
## 45.71429 3.851814 4.837013
## 45.85714 2.792781 3.786215
## 46.00000 3.543556 4.537595
## 46.14286 2.742260 3.741306
## 46.28571 3.300297 4.299788
## 46.42857 2.694133 3.696683
## 46.57143 3.108632 4.111503
## 46.71429 2.650097 3.654845
## 46.85714 2.957754 3.962730
## 
## 
## $高知県
## $高知県$mean
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##  [1] 0.1434029 0.1659745 0.1868390 0.2061256 0.2239535 0.2404331 0.2556664
##  [8] 0.2697476 0.2827638 0.2947957 0.3059176 0.3161983 0.3257015 0.3344860
## 
## $高知県$lower
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##                80%       95%
## 45.00000 -1.545884 -2.440139
## 45.14286 -1.544973 -2.450694
## 45.28571 -1.542401 -2.457806
## 45.42857 -1.538593 -2.462192
## 45.57143 -1.533883 -2.464426
## 45.71429 -1.528535 -2.464971
## 45.85714 -1.522758 -2.464200
## 46.00000 -1.516718 -2.462415
## 46.14286 -1.510543 -2.459863
## 46.28571 -1.504337 -2.456740
## 46.42857 -1.498177 -2.453208
## 46.57143 -1.492126 -2.449396
## 46.71429 -1.486229 -2.445408
## 46.85714 -1.480521 -2.441327
## 
## $高知県$upper
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##               80%      95%
## 45.00000 1.832690 2.726944
## 45.14286 1.876922 2.782643
## 45.28571 1.916079 2.831484
## 45.42857 1.950844 2.874443
## 45.57143 1.981790 2.912333
## 45.71429 2.009402 2.945837
## 45.85714 2.034091 2.975533
## 46.00000 2.056213 3.001911
## 46.14286 2.076071 3.025390
## 46.28571 2.093928 3.046331
## 46.42857 2.110013 3.065043
## 46.57143 2.124523 3.081792
## 46.71429 2.137632 3.096811
## 46.85714 2.149493 3.110299
## 
## 
## $福岡県
## $福岡県$mean
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##  [1] 13.698163 12.299781 11.641113  7.896894  8.131728  8.113516 11.464799
##  [8] 12.495182 11.994286 10.592822  8.528334  8.631862  8.676724  9.726081
## 
## $福岡県$lower
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##                  80%        95%
## 45.00000   0.2088488  -6.931963
## 45.14286  -3.5498368 -11.940118
## 45.28571  -5.4886930 -14.556665
## 45.42857  -9.9323308 -19.370552
## 45.57143 -10.3064327 -20.067006
## 45.71429 -11.2120419 -21.442374
## 45.85714  -9.2011700 -20.141073
## 46.00000 -10.7455847 -23.048503
## 46.14286 -13.0713473 -26.340292
## 46.28571 -15.6892734 -29.602174
## 46.42857 -18.5470152 -32.879839
## 46.57143 -19.1323019 -33.829762
## 46.71429 -19.9462808 -35.098384
## 46.85714 -20.0649785 -35.835413
## 
## $福岡県$upper
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##               80%      95%
## 45.00000 27.18748 34.32829
## 45.14286 28.14940 36.53968
## 45.28571 28.77092 37.83889
## 45.42857 25.72612 35.16434
## 45.57143 26.56989 36.33046
## 45.71429 27.43907 37.66941
## 45.85714 32.13077 43.07067
## 46.00000 35.73595 48.03887
## 46.14286 37.05992 50.32886
## 46.28571 36.87492 50.78782
## 46.42857 35.60368 49.93651
## 46.57143 36.39603 51.09349
## 46.71429 37.29973 52.45183
## 46.85714 39.51714 55.28758
## 
## 
## $佐賀県
## $佐賀県$mean
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##  [1] 0.9600406 0.9600406 0.9600406 0.9600406 0.9600406 0.9600406 0.9600406
##  [8] 0.9600406 0.9600406 0.9600406 0.9600406 0.9600406 0.9600406 0.9600406
## 
## $佐賀県$lower
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##                80%       95%
## 45.00000 -1.029008 -2.081947
## 45.14286 -1.103821 -2.196363
## 45.28571 -1.176015 -2.306774
## 45.42857 -1.245847 -2.413574
## 45.57143 -1.313536 -2.517095
## 45.71429 -1.379267 -2.617621
## 45.85714 -1.443201 -2.715400
## 46.00000 -1.505477 -2.810643
## 46.14286 -1.566219 -2.903540
## 46.28571 -1.625534 -2.994254
## 46.42857 -1.683518 -3.082934
## 46.57143 -1.740258 -3.169710
## 46.71429 -1.795830 -3.254700
## 46.85714 -1.850303 -3.338009
## 
## $佐賀県$upper
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##               80%      95%
## 45.00000 2.949089 4.002028
## 45.14286 3.023902 4.116444
## 45.28571 3.096096 4.226855
## 45.42857 3.165928 4.333655
## 45.57143 3.233617 4.437176
## 45.71429 3.299348 4.537703
## 45.85714 3.363282 4.635481
## 46.00000 3.425558 4.730724
## 46.14286 3.486300 4.823621
## 46.28571 3.545615 4.914335
## 46.42857 3.603600 5.003015
## 46.57143 3.660339 5.089791
## 46.71429 3.715911 5.174781
## 46.85714 3.770384 5.258090
## 
## 
## $長崎県
## $長崎県$mean
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##  [1] 0.4867392 0.8378998 1.0688412 0.6457477 0.8386527 0.6669740 0.8475475
##  [8] 0.7548787 0.7780170 0.8357706 0.7848681 0.9097870 0.7874213 0.9113932
## 
## $長崎県$lower
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##                80%       95%
## 45.00000 -1.636282 -2.760142
## 45.14286 -1.523271 -2.773199
## 45.28571 -1.779250 -3.286939
## 45.42857 -2.487341 -4.145898
## 45.57143 -2.614931 -4.443147
## 45.71429 -3.051123 -5.019364
## 45.85714 -3.131101 -5.237271
## 46.00000 -3.611763 -5.923324
## 46.14286 -3.873456 -6.335796
## 46.28571 -4.117881 -6.740186
## 46.42857 -4.436937 -7.201194
## 46.57143 -4.575139 -7.478683
## 46.71429 -4.944588 -7.978931
## 46.85714 -5.059438 -8.220206
## 
## $長崎県$upper
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##               80%       95%
## 45.00000 2.609761  3.733620
## 45.14286 3.199071  4.448999
## 45.28571 3.916933  5.424621
## 45.42857 3.778837  5.437394
## 45.57143 4.292236  6.120453
## 45.71429 4.385071  6.353312
## 45.85714 4.826196  6.932366
## 46.00000 5.121521  7.433081
## 46.14286 5.429490  7.891830
## 46.28571 5.789423  8.411727
## 46.42857 6.006673  8.770930
## 46.57143 6.394713  9.298257
## 46.71429 6.519431  9.553773
## 46.85714 6.882225 10.042992
## 
## 
## $熊本県
## $熊本県$mean
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##  [1] 14.54894 10.46381 11.48628 11.48628 11.48628 11.48628 11.48628 11.48628
##  [9] 11.48628 11.48628 11.48628 11.48628 11.48628 11.48628
## 
## $熊本県$lower
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##                80%        95%
## 45.00000 10.258485  7.9872536
## 45.14286  4.788777  1.7845944
## 45.28571  5.666387  2.5855218
## 45.42857  5.329599  2.0704499
## 45.57143  5.010303  1.5821285
## 45.71429  4.706027  1.1167778
## 45.85714  4.414831  0.6714320
## 46.00000  4.135161  0.2437137
## 46.14286  3.865748 -0.1683181
## 46.28571  3.605539 -0.5662723
## 46.42857  3.353652 -0.9515002
## 46.57143  3.109336 -1.3251500
## 46.71429  2.871946 -1.6882066
## 46.85714  2.640925 -2.0415231
## 
## $熊本県$upper
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##               80%      95%
## 45.00000 18.83940 21.11063
## 45.14286 16.13885 19.14303
## 45.28571 17.30617 20.38704
## 45.42857 17.64296 20.90211
## 45.57143 17.96226 21.39043
## 45.71429 18.26653 21.85578
## 45.85714 18.55773 22.30113
## 46.00000 18.83740 22.72884
## 46.14286 19.10681 23.14088
## 46.28571 19.36702 23.53883
## 46.42857 19.61891 23.92406
## 46.57143 19.86322 24.29771
## 46.71429 20.10061 24.66076
## 46.85714 20.33163 25.01408
## 
## 
## $大分県
## $大分県$mean
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##  [1] 7.8095004 5.5584113 3.9054253 2.9693426 2.1363663 2.0727669 2.4659124
##  [8] 1.8940220 1.5032634 1.2362678 1.0538362 0.9291852 0.8440143 0.7858191
## 
## $大分県$lower
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##                  80%        95%
## 45.00000  6.25948739  5.4389601
## 45.14286  3.68112531  2.6873501
## 45.28571  1.89349391  0.8284418
## 45.42857  0.89754412 -0.1991997
## 45.57143  0.03720253 -1.0740276
## 45.71429 -0.03905137 -1.1569804
## 45.85714  0.34821204 -0.7728308
## 46.00000 -0.24070247 -1.3707573
## 46.14286 -0.63936270 -1.7736005
## 46.28571 -0.91003744 -2.0462228
## 46.42857 -1.09418447 -2.2312779
## 46.57143 -1.21963588 -2.3571530
## 46.71429 -1.30518041 -2.4428953
## 46.85714 -1.36355001 -2.5013573
## 
## $大分県$upper
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##               80%       95%
## 45.00000 9.359513 10.180041
## 45.14286 7.435697  8.429473
## 45.28571 5.917357  6.982409
## 45.42857 5.041141  6.137885
## 45.57143 4.235530  5.346760
## 45.71429 4.184585  5.302514
## 45.85714 4.583613  5.704655
## 46.00000 4.028746  5.158801
## 46.14286 3.645890  4.780127
## 46.28571 3.382573  4.518758
## 46.42857 3.201857  4.338950
## 46.57143 3.078006  4.215523
## 46.71429 2.993209  4.130924
## 46.85714 2.935188  4.072995
## 
## 
## $宮崎県
## $宮崎県$mean
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##  [1] 0.7544347 0.8570800 0.8731911 0.8577549 0.8725445 0.8583744 0.8719509
##  [8] 0.8589431 0.8714060 0.8594651 0.8709058 0.8599444 0.8704467 0.8603843
## 
## $宮崎県$lower
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##                80%       95%
## 45.00000 -1.910504 -3.321238
## 45.14286 -1.927300 -3.401262
## 45.28571 -2.266041 -3.927851
## 45.42857 -2.529162 -4.322087
## 45.57143 -2.808705 -4.757440
## 45.71429 -3.038583 -5.101508
## 45.85714 -3.280991 -5.479425
## 46.00000 -3.488293 -5.789581
## 46.14286 -3.704735 -6.127197
## 46.28571 -3.895338 -6.412378
## 46.42857 -4.092385 -6.719792
## 46.57143 -4.269937 -6.985532
## 46.71429 -4.451837 -7.269284
## 46.85714 -4.618789 -7.519288
## 
## $宮崎県$upper
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##               80%      95%
## 45.00000 3.419374 4.830108
## 45.14286 3.641460 5.115421
## 45.28571 4.012423 5.674233
## 45.42857 4.244671 6.037597
## 45.57143 4.553793 6.502529
## 45.71429 4.755332 6.818257
## 45.85714 5.024892 7.223327
## 46.00000 5.206179 7.507467
## 46.14286 5.447547 7.870009
## 46.28571 5.614268 8.131309
## 46.42857 5.834197 8.461604
## 46.57143 5.989825 8.705420
## 46.71429 6.192730 9.010177
## 46.85714 6.339558 9.240057
## 
## 
## $鹿児島県
## $鹿児島県$mean
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##  [1] 2.459134 2.734894 2.900518 2.999993 3.059738 3.095622 3.117174 3.130118
##  [9] 3.137892 3.142562 3.145366 3.147051 3.148062 3.148670
## 
## $鹿児島県$lower
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##                80%       95%
## 45.00000 -1.508105 -3.608235
## 45.14286 -1.956951 -4.440663
## 45.28571 -2.065280 -4.694015
## 45.42857 -2.085975 -4.778324
## 45.57143 -2.086032 -4.810038
## 45.71429 -2.084082 -4.826052
## 45.85714 -2.084506 -4.838109
## 46.00000 -2.087617 -4.849719
## 46.14286 -2.092787 -4.861742
## 46.28571 -2.099352 -4.874254
## 46.42857 -2.106808 -4.887142
## 46.57143 -2.114815 -4.900278
## 46.71429 -2.123154 -4.913568
## 46.85714 -2.131691 -4.926945
## 
## $鹿児島県$upper
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##               80%       95%
## 45.00000 6.426374  8.526504
## 45.14286 7.426739  9.910452
## 45.28571 7.866316 10.495050
## 45.42857 8.085961 10.778310
## 45.57143 8.205508 10.929514
## 45.71429 8.275326 11.017295
## 45.85714 8.318854 11.072457
## 46.00000 8.347853 11.109955
## 46.14286 8.368572 11.137527
## 46.28571 8.384476 11.159378
## 46.42857 8.397541 11.177874
## 46.57143 8.408916 11.194380
## 46.71429 8.419279 11.209693
## 46.85714 8.429031 11.224285
## 
## 
## $沖縄県
## $沖縄県$mean
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##  [1] 32.31285 28.93876 35.04603 29.11048 33.94437 29.91934 28.91816 30.21394
##  [9] 30.21394 30.21394 30.21394 30.21394 30.21394 30.21394
## 
## $沖縄県$lower
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##                80%        95%
## 45.00000 20.469623 14.2001980
## 45.14286 16.041994  9.2148582
## 45.28571 21.175511 13.8329038
## 45.42857 14.330230  6.5060382
## 45.57143 18.307223 10.0294166
## 45.71429 13.469870  4.7620478
## 45.85714 11.694646  2.5770656
## 46.00000 11.270961  1.2431543
## 46.14286 10.249560 -0.3189427
## 46.28571  9.277932 -1.8049204
## 46.42857  8.349438 -3.2249285
## 46.57143  7.458799 -4.5870431
## 46.71429  6.601731 -5.8978161
## 46.85714  5.774701 -7.1626493
## 
## $沖縄県$upper
## Time Series:
## Start = c(45, 1) 
## End = c(46, 7) 
## Frequency = 7 
##               80%      95%
## 45.00000 44.15607 50.42550
## 45.14286 41.83553 48.66266
## 45.28571 48.91654 56.25915
## 45.42857 43.89073 51.71492
## 45.57143 49.58152 57.85932
## 45.71429 46.36881 55.07663
## 45.85714 46.14168 55.25926
## 46.00000 49.15691 59.18472
## 46.14286 50.17831 60.74682
## 46.28571 51.14994 62.23280
## 46.42857 52.07844 63.65280
## 46.57143 52.96908 65.01492
## 46.71429 53.82614 66.32569
## 46.85714 54.65317 67.59052